Differential Effects of Multisystemic Factors on the Developmental Trajectories of Emotion Regulation
Bibliographic record
Abstract
The current study investigated the development of emotion regulation (i.e., managing one’s emotions in order to meet a goal; Gross et al., 2019) within a multisystemic context across the sensitive periods of adolescence and emerging adulthood. It adds to current literature by including the entire sensitive period (i.e., age 12-29 years), incorporating the influence of multisystemic factors on different emotion regulation developmental pathways, and extending investigations of posttraumatic adjustment within an integrated framework. Participants were a subsample from the National Longitudinal Study of Adolescent Health (Add Health; N = 13414), a longitudinal nationally representative database that follows youth from adolescence into adulthood. Growth mixture modelling was applied to elicit unique trajectories of emotion regulation development (i.e., depressive symptoms) each with unique relationships to multisystemic covariates (i.e., biological sex, pubertal timing, self-esteem, adverse childhood experiences [ACEs], parent closeness, friendship support, parental socioeconomic status, neighbourhood safety, changes in household parental figures). Four non-linear trajectories were found: low (normative), low-increasing, increasing-decreasing, and high-decreasing. The multisystemic factors had differential effects on each pathway that, with the exception of ACEs, tend to wane in influence as youth age. Self-esteem, perceived parental closeness, and perceived neighbourhood safety most tended to be protective, whereas seeking support from a friend, experiencing (an) ACE(s), and changes to household parental figures tended to confer vulnerability. Emotion regulation development and posttraumatic adjustment do vary, with lasting impacts. A multisystemic, integrated framework showed what factors confer risk or protection within these trajectories, and helps explain previously mixed findings. Implications for identifying at-risk youth, preventative measures, and intervention are also discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".