{"component": "clause", "props": {"groups": [{"size": 2, "snippet_links": [{"key": "mental-health", "type": "definition", "offset": [76, 89]}, {"key": "table-1", "type": "clause", "offset": [124, 131]}, {"key": "the-participants", "type": "clause", "offset": [180, 196]}, {"key": "the-criteria", "type": "clause", "offset": [201, 213]}, {"key": "axis-i-disorder", "type": "definition", "offset": [231, 246]}, {"key": "all-p", "type": "clause", "offset": [414, 419]}], "snippet": "At pretreatment the mean CES-D score for the samples was 24.71 (SD = 9.82). Mental health characteristics are summarized in Table 1. In both IC and WLC approximately two-thirds of the participants met the criteria for at least one axis I disorder. The most prevalent disorders were MDD (39%) and anxiety disorders (45%). Previous MDD was reported by 69% of the participants. There were no significant differences (all p", "samples": [{"hash": "a4Wx9JzW99E", "uri": "/contracts/a4Wx9JzW99E#preliminary-analyses", "label": "Not Applicable", "score": 19.0, "published": true}, {"hash": "Hus6d5IlXE", "uri": "/contracts/Hus6d5IlXE#preliminary-analyses", "label": "Not Applicable", "score": 19.0, "published": true}], "hash": "b5b1adf53f1684a9910f366951ed5895", "id": 1}, {"size": 2, "snippet_links": [{"key": "by-participants", "type": "clause", "offset": [81, 96]}, {"key": "point-value", "type": "definition", "offset": [127, 138]}, {"key": "the-participant", "type": "clause", "offset": [178, 193]}, {"key": "scoring-system", "type": "definition", "offset": [496, 510]}, {"key": "standard-deviation", "type": "definition", "offset": [632, 650]}, {"key": "social-workers", "type": "definition", "offset": [721, 735]}], "snippet": "The measure utilized in this study contained three vignettes that were diagnosed by participants. Each vignette was assigned a point value dependent on the diagnosis provided by the participant. The point values for each vignette ranged from 0 to 2 points. Two points were given for a diagnosis that matched the correct diagnosis for the vignette. One point was given to diagnoses that were in the same DSM-5 category as the correct diagnosis. Zero points were given to all other diagnoses. This scoring system yielded a cumulative score with a range of 0 to 6 points. The overall average cumulative score was 3.17 points while the standard deviation was 1.38 points. Discipline Mean Standard Deviation Overall 3.17 1.38 Social Workers 3.13 1.31 Counselors 3.23 1.36 To test the hypothesis that a significant difference existed between psychologists, counselors, and social workers in regards to diagnosing, an One-Way Analysis of Variance", "samples": [{"hash": "1OGxxKSMuGB", "uri": "/contracts/1OGxxKSMuGB#preliminary-analyses", "label": "Dissertation", "score": 30.4084604228, "published": true}, {"hash": "L3Qqp7P4rN", "uri": "/contracts/L3Qqp7P4rN#preliminary-analyses", "label": "Masters Thesis", "score": 25.6639288159, "published": true}], "hash": "8181c083730919a1c9360bb64538e2fc", "id": 2}, {"size": 2, "snippet_links": [{"key": "changes-in", "type": "clause", "offset": [33, 43]}, {"key": "health-status", "type": "definition", "offset": [58, 71]}, {"key": "principal-component", "type": "clause", "offset": [201, 220]}, {"key": "accounting-for", "type": "clause", "offset": [221, 235]}, {"key": "the-data", "type": "clause", "offset": [266, 274]}, {"key": "method-a", "type": "clause", "offset": [556, 564]}, {"key": "change-in", "type": "clause", "offset": [594, 603]}, {"key": "health-problems", "type": "clause", "offset": [626, 641]}, {"key": "table-1", "type": "clause", "offset": [1276, 1283]}, {"key": "an-overview", "type": "clause", "offset": [1284, 1295]}, {"key": "characteristics-of", "type": "definition", "offset": [1312, 1330]}, {"key": "demographic-variables", "type": "clause", "offset": [1410, 1431]}, {"key": "in-addition", "type": "clause", "offset": [1519, 1530]}, {"key": "quality-of-life", "type": "clause", "offset": [1658, 1673]}], "snippet": "Because it was hypothesized that changes in perception of health status (as assessed with the SAS, HA subscale of the IAS and RQ) are correlated, the changes on these variables were transformed in one principal component accounting for as much of the variability in the data as possible. To this end a principal component analysis (PCA) was conducted on the 12 week follow-up residualised change scores on these measures (obtained by statistically correcting the follow-up scores for any baseline differences on these measures). Next, using the regression method a composite fac- tor score for change in perception of current health problems was calculated. The PCA on the residualised gain scores on the SAS, HA subscale and RQ at fol- low-up clearly yielded a one-factor solution (eigenvalue 1.75) accounting for 58.4% of the variance. Factor loadings were high (respectively 0.70, 0.82 and 0.77). Since there were no significant differences between the major non-western groups in change score in perception of current health problems (data not shown), we decided to operationalise ethnicity as western versus non-western. The ethnic difference in changes in perception of health status was significant (t(295): -3.53, p<.001) and had a moderate effect size (d = 0.38). In Table 1 an overview is presented of characteristics of participants with a west- ern or non-western ethnicity. Except for gender, all demographic variables show a significant difference between residents of a western or non-western ethnicity. In addition, non-western residents reported more symptoms of fatigue, psychopatholo- gy, and post-traumatic stress and less health-related quality of life compared to west- ern participants at baseline.", "samples": [{"hash": "h8u7wpyNGmS", "uri": "/contracts/h8u7wpyNGmS#preliminary-analyses", "label": "Not Applicable", "score": 18.0, "published": true}, {"hash": "bnwF2oGF1f9", "uri": "/contracts/bnwF2oGF1f9#preliminary-analyses", "label": "Not Applicable", "score": 18.0, "published": true}], "hash": "a81da2ba4626566c4db90aa8a4696720", "id": 3}, {"size": 1, "snippet_links": [{"key": "table-2", "type": "clause", "offset": [0, 7]}, {"key": "associated-with", "type": "definition", "offset": [383, 398]}, {"key": "line-of", "type": "definition", "offset": [454, 461]}, {"key": "table-3", "type": "clause", "offset": [462, 469]}, {"key": "to-table", "type": "definition", "offset": [551, 559]}, {"key": "description-of", "type": "definition", "offset": [570, 584]}, {"key": "aim-1", "type": "definition", "offset": [606, 611]}, {"key": "table-4", "type": "definition", "offset": [617, 624]}, {"key": "number-of", "type": "clause", "offset": [660, 669]}, {"key": "table-5", "type": "definition", "offset": [725, 732]}, {"key": "education-level", "type": "clause", "offset": [1076, 1091]}, {"key": "table-6", "type": "clause", "offset": [1198, 1205]}, {"key": "complete-list", "type": "clause", "offset": [1212, 1225]}, {"key": "column-1", "type": "definition", "offset": [1320, 1328]}, {"key": "social-support", "type": "clause", "offset": [1854, 1868]}, {"key": "the-day", "type": "definition", "offset": [2377, 2384]}, {"key": "the-study", "type": "definition", "offset": [2408, 2417]}, {"key": "effect-of", "type": "definition", "offset": [2747, 2756]}, {"key": "table-7", "type": "clause", "offset": [2774, 2781]}, {"key": "significant-amount", "type": "definition", "offset": [2957, 2975]}, {"key": "relationship-between", "type": "clause", "offset": [3087, 3107]}, {"key": "time-of-day", "type": "definition", "offset": [3116, 3127]}, {"key": "day-of", "type": "clause", "offset": [3413, 3419]}, {"key": "level-1", "type": "clause", "offset": [3632, 3639]}, {"key": "in-relation-to", "type": "clause", "offset": [4831, 4845]}, {"key": "perceived-stress", "type": "clause", "offset": [11841, 11857]}, {"key": "the-current", "type": "clause", "offset": [14418, 14429]}, {"key": "previous-findings", "type": "clause", "offset": [14522, 14539]}, {"key": "first-time", "type": "definition", "offset": [14689, 14699]}, {"key": "methodological-approach", "type": "clause", "offset": [14744, 14767]}, {"key": "over-time", "type": "clause", "offset": [15184, 15193]}, {"key": "reliance-on", "type": "clause", "offset": [15262, 15273]}, {"key": "common-use", "type": "definition", "offset": [15308, 15318]}, {"key": "measurement-of", "type": "clause", "offset": [15331, 15345]}, {"key": "lack-of", "type": "clause", "offset": [15355, 15362]}, {"key": "nine-months", "type": "definition", "offset": [16039, 16050]}, {"key": "and-higher", "type": "clause", "offset": [16089, 16099]}], "snippet": "Table 2 shows means and standard deviations of baseline variables. We found no significant differences on baseline measures by infant sex. Mothers completed an average of 16.64 (SD = 8.79) surveys, or 59.4% of all possible surveys, with a range from 3.6% to 100% completion. Response rate was significantly correlated with only one of the baseline variables; lower response rate was associated with higher baseline maternal symptoms of anxiety (see last line of Table 3). Table 3 also shows correlations among all of the baseline variables; we return to Table 3 for the description of findings relevant to Aim 1. See Table 4 for means, standard deviations and number of surveys completed for all EMA variables by time point. Table 5 displays correlations among EMA variables across all survey timepoints. All hypothesized predictor variables (symptoms of depression and anxiety, positive mood, and negative mood) were significantly intercorrelated. As such, we ran separate models for each predictor. Preliminary analyses regarding potential covariates revealed that maternal education level exerted a significant influence on PSE and was therefore included as a covariate in all HLM analyses. See Table 6 for a complete list of variables we examined as potential covariates. and infant characteristics, were mixed (see column 1 in Table 3). In partial support of Hypothesis 1A, PSE (labeled as PSOC in Table 3) was significantly, negatively associated with the DASS depression scale, r = -0.24, p =.003, but was not significantly associated with the DASS anxiety scale. In partial support of Hypothesis 1B, that baseline PSE would be associated with a set of maternal and infant characteristics, baseline PSE was significantly, negatively associated with the DASS stress scale, r = - 0.27, p = 0.001, but was not significantly associated with perceived social support or sleep quality. Further, PSE was significantly associated with two of the three domains of infant temperament, in the predicted directions: positively associated with effortful control and negatively associated with negative affect, r = 0.23, p = 0.004, and r = -0.25, p = 0.002, respectively, but not significantly associated with the surgency domain of infant temperament Next, we describe results from the HLM analyses of the EMA data to test Hypothesis 2, that PSE would be variable over the course of the day and over the course of the study. We ran an intercept-only model which revealed that the mean level of PSE across all time points was 15.24 (SD = 1.07). The intraclass correlation for PSE was 0.55, suggesting that 55% of the variation in PSE was attributable to the person and 45% was attributable to fluctuations within-persons. Next, we used HLM to assess the effect of time on PSE (see Table 7). Our hypothesis was supported, as PSE increased significantly over the course of the day, \u03b2 = 0.12, p < 0.001 and over the days of the study, \u03b2 = 0.08, p = 0.03. There was a significant amount of within-person variability, \u03c32 = 3.52, \u03c72 (df = 146) = 4007.11, p < 0.001, f2 = 0.03, (1 \u2013 \u03b2) = 0.58, in the relationship between PSE and time of day. There was also a significant amount of between person variability, \u03c32 = 5.54, \u03c72 (df = 129) = 1423.43.00, p < 0.001, f2 = 0.13, (1 \u2013 \u03b2) = 1.00, and within person variability, \u03c32 = 3.17, \u03c72 (df = 130) = 387.86, p = 0.001, f2 = 0.01, (1 \u2013 \u03b2) = 0.23, in the relationship between PSE and day of the study. To test Hypothesis 3a, that variability in PSE would be associated with variability in symptoms of depression and anxiety, we entered maternal symptoms of depression and anxiety (person centered) as a Level 1 predictor in the following model: Maternal PSE = \u03b20 + \u2587\u2587 (\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587 depression/anxiety) + error Results supported Hypothesis 3a in that maternal depression and anxiety exerted a significant influence on PSE across all time points, \u03b2 = -0.32, p < 0.001 (see Table 7). There was significant between-person variability, \u03c32 = 5.39, \u03c72 (df = 130) = 4065.11, p < 0.001, f2 = 0.13, (1 \u2013 \u03b2) = 0.99, and within-person variability, \u03c32 = \u2587.\u2587\u2587, \u2587\u2587 (\u2587\u2587 = 131) = 260.37, p < 0.001, f2 = 0.04, (1 \u2013 \u03b2) = 0.71, in the relationship between PSE and maternal depression and anxiety. Next, to test Hypothesis 3b, that variability in PSE would be associated with variability in symptoms of anxiety and depression over the course of the day, we entered maternal symptoms of depression and anxiety (person centered) and time of day (uncentered) as Level 1 predictors of parenting self-efficacy in the following model: Maternal PSE = \u03b20 + \u2587\u2587 (\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587 depression/anxiety) + \u03b22 (time of day) + error The results supported Hypothesis 3b in that both maternal depression/anxiety and time of day significantly predicted PSE, such that PSE increased slightly over the course of the day, \u03b2 = 0.08, p = 0.02, and decreased in relation to increases in symptoms of depression/anxiety, \u03b2 = -0.32, p < 0.001 (see Table 7). There was a significant amount of between person variability, \u03c32 = 5.39, \u03c72 (df = 130) = 4075.00, p < 0.001, f2 = 0.13, (1 \u2013 \u03b2) = 0.99, and within person variability, \u03c32 = 3.22, \u03c72 (df = 131) = 261.71, p < 0.001, f2 = 0.04, (1 \u2013 \u03b2) = 0.70. Next, to test Hypothesis 3c, that variability in PSE would be associated with variability in symptoms of anxiety and depression over the days of the study, we entered maternal symptoms of depression and anxiety (person centered) and day (uncentered) as Level 1 predictors of parenting self- efficacy in the following model: Maternal PSE = \u03b20 + \u2587\u2587 (\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587 depression/anxiety) + \u03b22 (day) + error Results from testing this model failed to support Hypothesis 3c in that, when considering symptoms of depression and anxiety and day in the same model, maternal depression/anxiety significantly predicted PSE, such that PSE decreased in relation to increases in symptoms of depression/anxiety, \u03b2 = -0.33, p < 0.001, but day of the study did not (see Table 7). However, there was a significant amount of between person variability, \u03c32 = 5.41, \u03c72 (df = 119) = 1276.27, p < 0.001, f2 = 0.26, (1 \u2013 \u03b2) = 1.00, and within person variability, \u03c32 = 2.89, \u03c72 (df = 120) = 240.28, p < 0.001, f2 = 0.03, (1 \u2013 \u03b2) = 0.58, in the relationship between PSE and symptoms of depression/anxiety across the days of the study, which provides partial support for Hypothesis 3b. To test Hypothesis 4a, that variability in PSE would be associated with variability in positive mood, we entered maternal positive mood (person and grand mean centered) as a Level 1 predictor in the following model: Maternal PSE = \u03b20 + \u2587\u2587 (\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587 positive mood) + error Results of testing this model revealed support for Hypothesis 4 in that maternal positive mood exerted a significant influence on PSE across all time points, \u03b2 = 0.88, p < 0.001 (see Table 7). There was significant between person variability, \u03c32 = 5.46, \u03c72 (df = 140) = 4418.32, p < 0.001, f2 = 0.17, (1 \u2013 \u03b2) = 1.00, and within-person variability, \u03c32 = \u2587.\u2587\u2587, \u2587\u2587 (\u2587\u2587 = 141) = 324.40, p < 0.001, f2 = 0.02, (1 \u2013 \u03b2) = 0.42, in the relationship between positive mood and PSE. Next, to test Hypotheses 4b, that variability in PSE would be associated with variability in positive mood over the course of the day, we entered maternal positive mood (person and grand mean centered) and time of day (uncentered) as Level 1 predictors of parenting self-efficacy in the following model: Maternal PSE = \u03b20 + \u2587\u2587 (\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587 positive mood) + \u03b22 (time of day) + error We found support for Hypotheses 4b in that both maternal positive mood and time of day significantly predicted PSE, such that PSE increased slightly over the course of the day, \u03b2 = 0.11, p = 0.02, and increased in relation to increases in positive mood, \u03b2 = 0.90, p < 0.001 (see Table 7). There was significant between person variability, \u03c32 = 5.45, \u03c72 (df = 140) = 4436.77, p < 0.001, f2 = 0.17, (1 \u2013 \u03b2) = 1.00, and within-person variability, \u03c32 = \u2587.\u2587\u2587, \u2587\u2587 (\u2587\u2587 = 141) = 322.63, p < 0.001, f2 = 0.03, (1 \u2013 \u03b2) = 0.58, in the relationship between positive mood and PSE over the course of the day. Next, to test Hypotheses 4c, that variability in PSE would be associated with variability in positive mood across the days of the study, we entered maternal positive mood (person centered) and day (uncentered) as Level 1 predictors of parenting self-efficacy in the following model: Maternal PSE = \u03b20 + \u2587\u2587 (\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587 positive mood) + \u03b22 (day) + error Results of testing this model revealed support for Hypothesis 4c in that both maternal positive mood and day of the study significantly predicted PSE, such that PSE increased slightly over the course of the day, \u03b2 = 0.06, p = 0.03, and increased in relation to increases in positive mood, \u03b2 = 0.90, p < 0.001 (see Table 7). There was significant between person variability, \u03c32 = 5.39, \u03c72 (df = 129) = 1365.67, p < 0.001, f2 = 0.28, (1 \u2013 \u03b2) = 1.00, and within-person variability, \u03c32 = \u2587.\u2587\u2587, \u2587\u2587 (\u2587\u2587 = 141) = 280.85, p < 0.001, f2 = 0.04, (1 \u2013 \u03b2) = 0.70, in the relationship between positive mood and PSE across the days of the study. To test Hypothesis 5a, that variability in PSE would be associated with variability in negative mood, we entered maternal negative mood (person and grand mean centered) as a Level 1 predictor in the following model: Maternal PSE = \u03b20 + \u2587\u2587 (\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587 negative mood) + error Results of testing this model revealed support for Hypothesis 5a in that maternal negative mood exerted a significant influence on PSE across all time points, \u03b2 = -0.99, p < 0.001 (see Table 7). There was significant between person variability, \u03c32 = 5.50, \u03c72 (df = 140) = 4580.18, p < 0.001, f2 = 0.19, (1 \u2013 \u03b2) = 1.00, and within-person variability, \u03c32 = \u2587.\u2587\u2587, \u2587\u2587 (\u2587\u2587 = 141) = 312.50, p < 0.001, f2 = 0.02, (1 \u2013 \u03b2) = 0.42, in the relationship between negative mood and PSE. Next, to test Hypothesis 5b, that variability in PSE would be associated with variability in negative mood over the course of the day, we entered maternal negative mood (person and grand mean centered) and time of day (uncentered) as Level 1 predictors of parenting self-efficacy in the following model: Maternal PSE = \u03b20 + \u2587\u2587 (\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587 negative mood) + \u03b22 (time of day) + error Results of testing this model did not reveal support for Hypothesis 5b; while maternal negative mood significantly predicted PSE, such that PSE decreased in relation to increases in negative mood, \u03b2 = -0.97, p = < 0.001, time of day did not (see Table 7). However, there was significant between person variability, \u03c32 = 5.50, \u03c72 (df = 140) = 4582.40, p < 0.001, f2 = 0.19, (1 \u2013 \u03b2) = 1.00, and within-person variability, \u03c32 = \u2587.\u2587\u2587, \u2587\u2587 (\u2587\u2587 = 141) = 312.02, p < 0.001, f2 = 0.02, (1 \u2013 \u03b2) = 0.42, in the relationship between negative mood and PSE over the course of the day which provides partial support for Hypothesis 5b. Next, to test Hypothesis 5c, that variability in PSE would be associated with variability in negative mood over the course of the study, we entered maternal negative mood (person and grand mean centered) and day (uncentered) as Level 1 predictors of parenting self-efficacy in the following model: Maternal PSE = \u03b20 + \u2587\u2587 (\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587 negative mood) + \u03b22 (day) + error Results of testing this model did not reveal support for Hypothesis 5c; while maternal negative mood significantly predicted PSE, such that PSE decreased in relation to increases in negative mood, \u03b2 = -1.02, p < 0.001, day of the study did not (see Table 7). However, there was significant between person variability, \u03c32 = 5.45, \u03c72 (df = 127) = 1482.90, p < 0.001, f2 = 0.32, (1 \u2013 \u03b2) = 1.00, and within-person variability, \u03c32 = \u2587.\u2587\u2587, \u2587\u2587 (\u2587\u2587 = 128) = 282.39, p < 0.001, f2 = 0.03, (1 \u2013 \u03b2) = 0.58, in the relationship between negative mood and PSE across the days of the study, which provides partial support for Hypothesis 5c. To test hypotheses 6a, that variability in PSE would be associated with variability in perceived stress, we entered maternal perceived stress (person centered) as a Level 1 predictor in the following model: Maternal PSE = \u03b20 + \u2587\u2587 (\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587 perceived stress) + error Results of testing this model revealed support for Hypothesis 6a, such that maternal perceived stress exerted a significant influence on PSE across all time points, \u03b2 = -0.24, p < 0.001 (see Table 7). There was a significant amount of between person variability, \u03c32 = 5.45, \u03c72 (df = 135) = 4026.95, p < 0.001, f2 = 0.16, (1 \u2013 \u03b2) = 1.00, and within person variability, \u03c32 = 3.14, \u03c72 (df = 136) = 219.45, p < 0.001 f2 = 0.03, (1 \u2013 \u03b2) = 0.58, in the relationship between perceived stress and PSE. Next, to test Hypothesis 6b, that variability in PSE would be associated with variability in perceived stress over the course of the day, we entered maternal perceived stress (person centered) and time of day (uncentered) as Level 1 predictors of parenting self-efficacy in the following model: Maternal PSE = \u03b20 + \u2587\u2587 (\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587 perceived stress) + \u03b22 (time of day) + error Results of testing this model revealed support for Hypothesis 6b, in that both maternal perceived stress and time of day significantly predicted PSE; PSE increased slightly over the course of the day, \u03b2 = 0.09, p = 0.02, and decreased in relation to increases in perceived stress, \u03b2 = -0.23, p < 0.001 (see Table 7). There was a significant amount of between person variability, \u03c32 = 5.44, \u03c72 (df = 135) = 4119.92, p < 0.001, f2 = 0.16, (1 \u2013 \u03b2) = 1.00, and within person variability, \u03c32 = 3.13, \u03c72 (df = 131) = 218.53, p < 0.001, f2 = 0.03, (1 \u2013 \u03b2) = 0.58, which provides partial support for Hypothesis 6b. Next, to test the Hypothesis 6c, that variability in PSE would be associated with variability in perceived stress over the course of the study, we entered maternal perceived stress (person centered) and day (uncentered) Level 1 predictors of parenting self-efficacy in the following model: Maternal PSE = \u03b20 + \u2587\u2587 (\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587 perceived stress) + \u03b22 (day) + error Results of testing this model did not reveal support for Hypothesis 6c; while maternal perceived stress significantly predicted PSE, such that PSE decreased in relation to increases in perceived stress, \u03b2 = -0.22, p < 0.001, day of the study did not (see Table 7). However, there was a significant amount of between person variability, \u03c32 = 5.41, \u03c72 (df = 130) = 1283.15, p < 0.001, f2 = 0.16, (1 \u2013 \u03b2) = 1.00, and within person variability, \u03c32 = 2.86, \u03c72 (df = 131) = 189.41, p < 0.001, f2 = 0.03, (1 \u2013 \u03b2) = 0.58, which provides partial support for Hypothesis 6c. The current study contributes to the literature on PSE in several ways. First, we built on and extended previous findings by examining cross-sectional associations among PSE and a host of theory- and empirically-justified potential maternal and infant characteristics in first time mothers of infants. Second, we used a novel methodological approach (daily mobile-based questionnaires) to empirically assess PSE as a dynamic construct. Third, with that same approach, we examined how fluctuations in symptoms of depression and anxiety, mood, and perceived stress were associated with PSE beliefs in the moment over the course of the day and across the days of the study. By investigating the dynamics of PSE and how maternal characteristics were associated with PSE over time, we addressed the following significant gaps in the literature: (1) reliance on cross-sectional associations, (2) common use of a single measurement of PSE, (3) lack of study of maternal characteristics that have been hypothesized to dynamically affect PSE. This novel approach allowed us to examine heretofore unanswered questions about the relationships between maternal depressive and anxiety symptoms, maternal positive and negative mood, perceived stress, and PSE beliefs in the moment and in ecologically valid settings. Our first aim was to investigate cross-sectional associations among PSE and a set of maternal and infant characteristics. First, we replicated previous findings of significant, negative associations between PSE and symptoms of depression and maternal stress in our sample of first-time mothers of infants aged four to nine months. Higher levels of depressive symptoms and higher stress levels were associated with lower PSE, with medium effect sizes. Second, we extended previous findings on PSE and infant temperament by examining the associations between PSE and three dimensions of infant temperament: negative affect, effortful control, and surgency. We found", "samples": [{"hash": "euVqli625Rk", "uri": "/contracts/euVqli625Rk#preliminary-analyses", "label": "Distribution Agreement", "score": 29.5142288753, "published": true}], "hash": "32bbf2202d44bfae1de36ef47f281372", "id": 4}, {"size": 1, "snippet_links": [{"key": "prior-to", "type": "definition", "offset": [216, 224]}, {"key": "hypothesis-testing", "type": "clause", "offset": [225, 243]}, {"key": "based-on", "type": "clause", "offset": [291, 299]}, {"key": "the-criteria", "type": "clause", "offset": [300, 312]}, {"key": "the-data", "type": "clause", "offset": [450, 458]}, {"key": "given-that", "type": "clause", "offset": [571, 581]}, {"key": "previous-research", "type": "clause", "offset": [582, 599]}, {"key": "associated-with", "type": "definition", "offset": [646, 661]}, {"key": "related-to", "type": "clause", "offset": [766, 776]}, {"key": "the-future", "type": "clause", "offset": [802, 812]}], "snippet": "A path analysis was conducted using Mplus 7.4 (\u2587\u2587\u2587\u2587\u2587\u2587 and \u2587\u2587\u2587\u2587\u2587\u2587, 1998-2015). MLM estimator was chosen for its robustness to non-normality in data that does not contain missing values (Muth\u00e9n and Muth\u00e9n, 1998-2015). Prior to hypothesis testing, an analysis of goodness of fit was conducted. Based on the criteria of goodness of fit by \u2587\u2587 and \u2587\u2587\u2587\u2587\u2587\u2587\u2587 (1999), RMSEA values lower than .08 and CFI and TLI values above .90 are indicators of good fits to the data. The hypothesized model did not fit the data well (RMSEA = .163, 90% CI = [0.130, 0.198], CFI = .95, TLI =.94). Given that previous research suggests that motives of entrepreneurship are associated with aspirations to grow a business (e.g., Verheul & \u2587\u2587\u2587 \u2587\u2587\u2587, 2011) and that motives of entrepreneurship are related to optimistic beliefs about the future (\u2587\u2587\u2587 \u2587\u2587\u2587 \u2587\u2587\u2587\u2587 et al., 2016), the hypothesized model was revised by adding direct paths from motives of entrepreneurship to future time perspective and general growth intentions. The analysis of goodness of fit revealed that the revised model fitted the data well (RMSEA = .060, 90% CI = [0.000, 0.0114], CFI = .99, TLI =.97;", "samples": [{"hash": "2JrIuw2OyU1", "uri": "/contracts/2JrIuw2OyU1#preliminary-analyses", "label": "Publication", "score": 23.523613963, "published": true}], "hash": "a85746011e3ac820489fd57235e3d25c", "id": 5}, {"size": 1, "snippet_links": [{"key": "baseline-model", "type": "definition", "offset": [196, 210]}, {"key": "dependent-variable", "type": "clause", "offset": [255, 273]}, {"key": "the-data", "type": "clause", "offset": [519, 527]}, {"key": "in-addition", "type": "clause", "offset": [529, 540]}, {"key": "the-model", "type": "clause", "offset": [650, 659]}, {"key": "residual-error", "type": "definition", "offset": [681, 695]}, {"key": "with-respect-to", "type": "clause", "offset": [849, 864]}, {"key": "associated-with", "type": "definition", "offset": [1300, 1315]}, {"key": "number-of", "type": "clause", "offset": [1320, 1329]}, {"key": "effect-of", "type": "definition", "offset": [1383, 1392]}, {"key": "work-and", "type": "clause", "offset": [1492, 1500]}, {"key": "private-events", "type": "definition", "offset": [1501, 1515]}, {"key": "work-related", "type": "clause", "offset": [1580, 1592]}, {"key": "duration-and", "type": "clause", "offset": [1817, 1829]}, {"key": "first-step", "type": "definition", "offset": [1931, 1941]}, {"key": "frequency-and", "type": "definition", "offset": [1970, 1983]}, {"key": "in-the-study", "type": "clause", "offset": [2019, 2031]}, {"key": "entered-into", "type": "clause", "offset": [2372, 2384]}, {"key": "related-to", "type": "clause", "offset": [2818, 2828]}, {"key": "the-mediation", "type": "definition", "offset": [3356, 3369]}, {"key": "final-model", "type": "clause", "offset": [3432, 3443]}, {"key": "education-level", "type": "clause", "offset": [3557, 3572]}, {"key": "alcohol-use", "type": "definition", "offset": [3583, 3594]}, {"key": "in-table-2", "type": "clause", "offset": [3732, 3742]}, {"key": "the-final", "type": "clause", "offset": [3747, 3756]}, {"key": "physical-effort", "type": "clause", "offset": [4440, 4455]}, {"key": "table-3", "type": "clause", "offset": [6315, 6322]}], "snippet": "To assess whether multilevel analysis would be appropriate to analyze the effects of stressful events and worry episodes on somatic complaints, we first estimated the intra-class correlation in a baseline model with a random intercept and with SHC as the dependent variable, but without any predictors. The results showed that the intraclass correlation was .59, showing that 59% of the variance was due to individual differences between participants, thereby providing evidence for a 2-level hierarchical structure of the data. In addition, since the somatic complaints were measured repeatedly within subjects, we tested whether the error terms of the model would be correlated. Residual error covariance was modeled using the first-order auto-regressive covariance matrix, which showed that the estimated auto-correlation (\u03c1) was .22 (p = .017). With respect to the model predicting daily negative affect, a baseline random intercept model without predictors showed that the intra-class correlation was .46, that 46% of the variance was due to individual differences between participants. Because residual error covariance using the first-order auto-regressive covariance matrix did not yield stable models, the diagonal covariance matrix was used. First we examined whether stressful events were associated with the number of SHC, while controlling for SHC the previous day. The effect of stressful events on SHC was significant (B = .191, p < .0001, 95% CI: .087 - .294). When stressful work and private events were entered separately into the model, the results showed that work related stressors had a larger effect on somatic complaints (B = .366, p < .01, 95% CI: .114 - .617) than private stressors (B = .290, p < .05, 95% CI: .052 - .527). Next, we examined the effects of the worry variables, (frequency, duration and intensity) on the number of SHC. The correlations between these variables were high (rs > .87). In a first step, SHC was regressed on worry frequency and worry duration (the variables used in the study by Brosschot & van der Doef [2006]), while controlling for the number of somatic complaints during the previous day. Worry frequency significantly predicted the number of somatic complaints (B = .451, p < .01, 95% CI: .152 - .749), and worry duration did this marginally (B = .008, p = .082, 95% CI: -.001 - .019). When worry intensity was entered into the model, only worry intensity predicted the number of SHC (B = .094, p < .01, 95% CI: .028 - .160), whereas the effects of worry frequency (B = .137, p = .460) and worry duration (B = .000, p = .954) were not significant anymore. To test whether the effect of stressful events on SHC was mediated by worry intensity, worry intensity was regressed on the number of stressful events. The number of stressful events was significantly related to worry intensity (B = 1.165, p < .0001, 95% CI: .863 - 1.467). In a subsequent model, stressful events and worry intensity were both added as predictors of SHC. When controlling for the effect of worry intensity, the effect of stressful events on SHC was reduced and became non- significant (B = .053, p = .337, 95% CI: -.055 - .161), whereas the effect of worry intensity was still significant (B = .106, p < .0001, 95% CI: .071 - .140), suggesting full mediation. \u2587\u2587\u2587\u2587\u2587\u2019\u2587 z-score of this mediated effect was 4.82 showing that the mediation effect was significant (p < .0001; \u2587\u2587\u2587\u2587\u2587 & \u2587\u2587\u2587\u2587\u2587, 1986). In a final model we tested whether worry intensity was related to SHC while controlling for biobehavioral variables (age, gender, education level, smoking, alcohol use, sleep quality, baseline traits and daily hassles during the last two months) and SHC during the previous day. The results are presented in Table 2. In the final model, SHC was predicted by worry intensity, time (days) and daily hassles. Table 2. Estimates of fixed effects predicting the number of somatic complaints with and without negative affect (baseline and daily measurements) B SE t p B SE t p Intercept 4.67 0.38 12.18 .00 4.68 0.36 12.97 .00 Stressful events -0.03 0.06 -0.43 .67 -0.05 0.06 -0.78 .44 Worry intensity 0.11 0.02 5.99 .00 0.10 0.02 4.88 .00 Age 0.00 0.02 0.05 .96 0.00 0.02 -0.01 .99 Gender 0.63 0.59 1.07 .29 0.48 0.55 0.87 .39 Education -0.68 0.67 -1.02 .32 -0.57 0.61 -0.92 .36 Caffeine 0.04 0.10 0.41 .68 0.05 0.10 0.52 .60 Smoking 0.10 0.06 1.77 .08 0.16 0.06 2.47 .01 Alcohol 0.10 0.10 0.96 .34 0.05 0.10 0.49 .62 Physical effort -0.10 0.12 -0.85 .40 -0.10 0.12 -0.83 .41 PSWQ -0.04 0.03 -1.58 .12 -0.04 0.03 -1.54 .13 DHC 0.03 0.01 2.24 .03 0.02 0.01 1.78 .08 Previous SHC 0.03 0.05 0.64 .52 0.06 0.05 1.11 .27 Sleep quality previous night -0.07 0.15 -0.47 .64 -0.18 0.15 -1.17 .24 Time -0.26 0.08 -3.43 .00 -0.23 0.08 -3.10 .00 Baseline negative affect -0.05 0.07 -0.71 .48 Daily Negative affect 0.14 0.04 3.37 .00 Deviance (-2 log likelihood) 1138.435 1103.456 First, negative affect was regressed on the number of stressful events, while controlling for negative affect during the previous day. Stressful events significantly predicted negative affect (B = 0.520, p < .0001, 95% CI: 0.334 - 0.706). Stressful events related to work were a slightly better predictor of negative affect (B = 0.934, p < .0001, 95% CI: 0.516 - 1.353) than stressful private events (B = 0.698, p < .0001, 95% CI: 0.308 - 1.089). Next, negative affect was regressed on worry frequency and worry duration. Only worry duration predicted negative affect (B = 0.022, p < .05, 95% CI: 0.005 - 0.038), whereas worry frequency did not (B = 0.382, p = .144). When worry intensity was entered into the model, only worry intensity predicted negative affect (B = .144, p < .05, 95% CI: 0.033 - 0.254), whereas the effects of worry frequency (B = -.065, p = .839) and worry duration (B = .010, p = .308) were not significant anymore. When controlling for the effect of negative affect during the previous day and worry intensity, the effect of stressful events on negative affect was still significant (B = .323, p < .01, 95% CI: 0.127 - .519), as was the effect of worry intensity (B = .131, p < .0001, 95% CI: .077 - .184). In the final model, negative affect was regressed on stressful events, worry intensity, the biobehavioral variables, negative affect at baseline and negative affect during the previous day (see Table 3). Negative affect was significantly predicted by negative affect at baseline (B = 0.154, p = .098, 95% CI: -0.030 - 0.338), worry intensity (B = 0.128, p < .0001, 95% CI: 0.069 - 0.187) and, yet marginally, by age (B = 0.057, p = .067, 95% CI: -0.064 - 0.366), but not any more by stressful events. Table 3. Estimates of fixed effects predicting daily negative affect. B SE t p Intercept 13.50 0.54 25.07 .00 Worry intensity 0.13 0.03 4.29 .00 Stressful events 0.15 0.11 1.38 .17 Age 0.06 0.03 1.90 .07 Gender 0.84 0.74 1.13 .27 Education -0.28 0.85 -0.34 .74 Caffeine -0.22 0.15 -1.48 .14 Smoking -0.06 0.09 -0.67 .51 Alcohol 0.09 0.15 0.59 .56 Physical effort -0.06 0.18 -0.31 .75 PSWQ 0.01 0.04 0.31 .76 DHC 0.02 0.02 0.81 .42 Negative Affect previous day 0.07 0.06 1.05 .30 Sleep quality previous night -0.05 0.23 -0.20 .84 Time -0.08 0.12 -0.66 .51 Baseline negative affect 0.15 0.09 1.71 .10", "samples": [{"hash": "hOQeGBcVQz0", "uri": "/contracts/hOQeGBcVQz0#preliminary-analyses", "label": "Thesis", "score": 19.0, "published": true}], "hash": "7b7c49e0b4b5a8c8f444189c37b0e589", "id": 6}, {"size": 1, "snippet_links": [{"key": "age-groups", "type": "definition", "offset": [44, 54]}, {"key": "demographic-variables", "type": "clause", "offset": [65, 86]}, {"key": "in-addition", "type": "clause", "offset": [399, 410]}, {"key": "reports-of", "type": "clause", "offset": [882, 892]}, {"key": "control-variables", "type": "clause", "offset": [914, 931]}, {"key": "additional-analyses", "type": "clause", "offset": [1047, 1066]}], "snippet": "We tested for differences between the three age groups regarding demographic variables in the Time 1 mother and father samples and in the longitudinal sample. The only differences between age groups were found for presence of siblings and parental educational level (Table 2.2). In the Time 1 mother sample and the Time 1 father sample, older children had siblings more often than younger children. In addition, 12-month-olds in the Time 1 mother sample had parents with a higher educational level than 36-month-olds. No differences between age groups on demographic variables existed in the longitudinal sample. Because age groups in the Time 1 mother sample differed on presence of siblings and parental educational level, analyses concerning age differences in this sample were controlled for the effects of these two variables. To facilitate comparison across mother and father reports of aggression, the same control variables were used in analyses concerning age effects in the Time 1 father sample. Some univariate outliers were found, but additional analyses showed that the outliers had no effects on the results. No multivariate outliers were identified.", "samples": [{"hash": "dPN8zMaBUQF", "uri": "/contracts/dPN8zMaBUQF#preliminary-analyses", "label": "Early Childhood Aggression", "score": 19.0, "published": true}], "hash": "48993ae7958bb95a72150bb9c6993790", "id": 7}, {"size": 1, "snippet_links": [{"key": "control-group", "type": "definition", "offset": [137, 150]}, {"key": "no-group", "type": "definition", "offset": [157, 165]}, {"key": "race-or-ethnicity", "type": "definition", "offset": [181, 198]}, {"key": "table-2", "type": "clause", "offset": [328, 335]}, {"key": "sample-characteristics", "type": "clause", "offset": [353, 375]}, {"key": "age-and-sex", "type": "clause", "offset": [517, 528]}, {"key": "to-determine", "type": "definition", "offset": [553, 565]}, {"key": "related-to", "type": "clause", "offset": [603, 613]}, {"key": "the-t", "type": "clause", "offset": [726, 731]}, {"key": "table-3", "type": "clause", "offset": [878, 885]}, {"key": "not-included", "type": "clause", "offset": [956, 968]}, {"key": "lack-of", "type": "clause", "offset": [1011, 1018]}], "snippet": "Chi squared analyses comparing CHR and HC groups revealed significant differences in sex ratio (larger proportion of males in CHR versus control group), but no group differences in race or ethnicity. T-tests revealed significant differences in age between groups (CHR mean = 18.5 years, healthy controls mean = 19.7 years). See Table 2 for a summary of sample characteristics including demographics and mean symptom ratings for the CHR and control groups. Remaining analyses comparing CHR and control groups included age and sex as covariates. In order to determine if medication status at baseline was related to the sleep variable (in the CHR participants), multiple independent samples T-tests were conducted. Results from the T-tests showed that there was no significant association between sleep disturbance and medication use of any kind, including sleep medications (see Table 3. for breakdown by medication status). Medication status was therefore not included as a covariate in remaining analyses. The lack of association between sleep disturbance and use of sleep medication likely indicates that the use of sleep medication is effective at reducing sleep disturbance.", "samples": [{"hash": "5jduUfUjWUO", "uri": "/contracts/5jduUfUjWUO#preliminary-analyses", "label": "Distribution Agreement", "score": 27.8444346514, "published": true}], "hash": "65db4126512b5083dd78078a5e3983f0", "id": 8}, {"size": 1, "snippet_links": [{"key": "control-condition", "type": "definition", "offset": [181, 198]}, {"key": "the-model", "type": "clause", "offset": [213, 222]}, {"key": "the-data", "type": "clause", "offset": [230, 238]}, {"key": "associated-with", "type": "definition", "offset": [420, 435]}], "snippet": "The analysis of goodness of fit of the revised hypothesized model that included variables representing experimental conditions (i.e., simplified version and extended version) and a control condition revealed that the model fitted the data well (RMSEA = .045, 90% CI = [0.000, 0.089], CFI = .98, TLI =.96; see Figure 4.2). Hypotheses 1 and 2. In support of Hypothesis 1, opportunity-based entrepreneurship was positively associated with entrepreneurial self-esteem (\u03b2 = .25, SE =.06, p < .01), whereas necessity-based entrepreneurship was negatively associated with entrepreneurial self-esteem (\u03b2 =", "samples": [{"hash": "2JrIuw2OyU1", "uri": "/contracts/2JrIuw2OyU1#preliminary-analyses", "label": "Publication", "score": 23.523613963, "published": true}], "hash": "538505671eb6b64f4a996031f2b5a985", "id": 9}, {"size": 1, "snippet_links": [{"key": "in-table-2", "type": "clause", "offset": [92, 102]}, {"key": "the-assumption", "type": "clause", "offset": [123, 137]}, {"key": "the-\u2587", "type": "clause", "offset": [207, 212]}, {"key": "level-1", "type": "clause", "offset": [523, 530]}, {"key": "in-order-to", "type": "clause", "offset": [531, 542]}, {"key": "the-study", "type": "definition", "offset": [607, 616]}, {"key": "rate-of", "type": "clause", "offset": [744, 751]}, {"key": "per-day", "type": "definition", "offset": [768, 775]}, {"key": "over-time", "type": "clause", "offset": [890, 899]}, {"key": "in-addition", "type": "clause", "offset": [919, 930]}, {"key": "effect-of", "type": "definition", "offset": [1061, 1070]}, {"key": "relationship-between", "type": "clause", "offset": [1090, 1110]}, {"key": "gestational-age", "type": "definition", "offset": [1171, 1186]}, {"key": "socioeconomic-status", "type": "clause", "offset": [1216, 1236]}, {"key": "associated-with", "type": "definition", "offset": [1355, 1370]}, {"key": "control-variables", "type": "clause", "offset": [1424, 1441]}, {"key": "related-to", "type": "clause", "offset": [1446, 1456]}, {"key": "the-intercept", "type": "clause", "offset": [2011, 2024]}, {"key": "other-variables", "type": "clause", "offset": [2059, 2074]}, {"key": "prior-to", "type": "definition", "offset": [2162, 2170]}, {"key": "regression-analysis", "type": "definition", "offset": [2224, 2243]}, {"key": "a-portion", "type": "definition", "offset": [2389, 2398]}, {"key": "the-mediation", "type": "definition", "offset": [2402, 2415]}, {"key": "table-3", "type": "clause", "offset": [2592, 2599]}, {"key": "level-2", "type": "definition", "offset": [2780, 2787]}, {"key": "table-4", "type": "definition", "offset": [3021, 3028]}, {"key": "component-parts", "type": "definition", "offset": [3114, 3129]}, {"key": "a-separate", "type": "definition", "offset": [3546, 3556]}, {"key": "term-of", "type": "clause", "offset": [4258, 4265]}, {"key": "the-individual", "type": "clause", "offset": [4584, 4598]}], "snippet": "Correlations between each predictor variable and infant weight at each time point are shown in Table 2. To examine whether the assumption of normality was met, infant weight at each time point was examined. The \u2587\u2587\u2587\u2587\u2587\u2587\u2587-\u2587\u2587\u2587\u2587 test of normality indicated that raw infant weight scores at all time points were normally distributed (ps > 0.05). Thus for all subsequent analyses in HLM, raw weight at each time point was used. First, an unconditional growth model was run using weight as the outcome and time as the predictor at level 1 in order to examine whether weight changed significantly over the course of the study. The estimated mean slope for the weight variable was 0.026 (SE=0.0007), indicating that infant weight increased at an average rate of 0.023 kilograms per day from birth to six-months-of-age. This was significant at p < 0.001, and indicated significant increases in weight over time within the sample. In addition, there was significant variation among slopes of weight gain in this sample (\u03c72=163.55; p < 0.001). Before examining the indirect effect of overfeeding on the relationship between maternal stress and RWG, associations between maternal age, gestational age, preterm status, infant sex, socioeconomic status, and overfeeding were examined using bivariate analyses. Results indicated that only preterm status was significantly associated with overfeeding (p = 0.046). None of the other potential control variables was related to overfeeding (ps > 0.05). When preparing to examine any main effects of maternal stress and overfeeding on weight gain, all potential covariates were each entered separately as predictors of infant weight (intercept and slope) in HLM. Results indicated that preterm status and gestational age were both significantly associated with birthweight (p < 0.001). Entering both variables as covariates would have been redundant, as preterm status is a dichotomized variable created from gestational age. Thus, only gestational age was entered as a covariate at the intercept in all following HLM analyses. No other variables were associated with either birthweight (i.e., intercept) or the slope of weight gain. Prior to examining any associations in HLM, a binary logistic regression analysis was conducted to examine whether maternal stress was associated with the overfeeding composite variable. A significant association would support a portion of the mediation hypothesis. Results indicated that maternal stress significantly predicted overfeeding, but in the opposite direction as predicted (OR = 0.94; 95% CI = 0.88-1.00; p < .05; See Table 3), such that higher maternal stress decreased the odds of being overfed. Two HLM models were then constructed with maternal stress and overfeeding entered as separate predictors at level 2 to test the main effects of each on weight gain. Results indicated that neither maternal stress nor the overfeeding composite variable were significant predictors of the slope of weight gain (p = 0.70 and p = 0.27, respectively; see Table 4). Continuing with analyses, the overfeeding variable was subsequently split into its component parts (i.e., formula feeding, amount of daily formula intake, early introduction of complementary foods) in order to test specific mechanisms that may be associated with weight gain. Using the same procedure as described above, maternal stress was not found to be associated with any of the three separate feeding variables (ps > 0.25). Three separate models were then created in HLM with each feeding variable entered as a separate predictor. The amount of daily formula intake variable excluded infants who were exclusively breastfed (n = 8) and statistical outliers (n = 5). Formula feeding and early introduction of complementary foods were not associated with the slope of weight gain (p = 0.07 and p = 0.98, respectively), although the former was trending towards significance. Amount of daily formula intake (in ounces) was a significant predictor of slope of weight gain, such that greater intake predicted more rapid weight gain (\u03b2 = 0.00013; p = 0.007; df = 61; SE = 0.00005). To test the moderation hypothesis, a binary logistic regression was conducted in SPSS using maternal stress, infant fussiness, and the interaction term of stress and fussiness as predictors of the overfeeding composite variable. Within this model, there were no significant main effects of stress or fussiness, nor a significant interaction between the two predictors (ps > 0.15). Similarly, no main effects or interactions of maternal stress and fussiness were found when the individual feeding variables were each examined as the outcome in separate regression analyses (ps > 0.26).", "samples": [{"hash": "bdKxaWZkr4c", "uri": "/contracts/bdKxaWZkr4c#preliminary-analyses", "label": "Distribution Agreement", "score": 32.1106734867, "published": true}], "hash": "5a2bb165f33d185c98af9bce8051b765", "id": 10}], "next_curs": "Cl0SV2oVc35sYXdpbnNpZGVyY29udHJhY3RzcjkLEhZDbGF1c2VTbmlwcGV0R3JvdXBfdjU2Ih1wcmVsaW1pbmFyeS1hbmFseXNlcyMwMDAwMDAwYQyiAQJlbhgAIAA=", "clause": {"size": 16, "parents": [], "title": "Preliminary Analyses", "children": [], "id": "preliminary-analyses", "related": [["preliminary-evaluation", "Preliminary Evaluation", "Preliminary Evaluation"], ["preliminary", "Preliminary", "Preliminary"], ["preliminary-examination", "Preliminary Examination", "Preliminary Examination"], ["quantitative-analysis", "Quantitative Analysis", "Quantitative Analysis"], ["special-analyses", "Special Analyses", "Special Analyses"]], "related_snippets": [], "updated": "2025-07-07T12:37:53+00:00"}, "json": true, "cursor": ""}}