What are we modeling? An evaluation of depressive symptom trajectory models from adolescence to early midlife in the Add Health cohort
Bibliographic record
Abstract
It is critical to understand the development of depressive symptoms across life stages. Existing research has primarily explored this from a life course perspective, yielding inconsistent depressive trajectories, and raising questions as to whether life course processes best characterize the evolution of depressive symptoms across life stages. This study compares ten longitudinal models from four theoretical perspectives ( life course , enduring , autoregressive , and hybrid ) to identify the best-fitting, theoretically-informed model of depressive symptom development from adolescence to early midlife. Results indicate a hybrid model that combines enduring and autoregressive perspectives outperforms traditional life course models and best fits the data. This hybrid model suggests depressive symptom levels at baseline remain relatively stable across life stages, with past symptom levels predicting future levels. Additionally, it reveals racial/ethnic and gender differences in symptom levels in early adolescence, as well as racial/ethnic differences in longitudinal patterns. These findings advance theoretical understanding of depressive symptom development among US young adults across early portions of the life course. • This study compares life course, enduring, autoregressive, and hybrid models of depressive symptoms in the Add Health cohort. • A hybrid model which combines enduring and autoregressive perspectives outperforms life course models of depressive development. • This model suggests baseline depressive levels stay stable across life stages, with past levels predicting future levels. • There are racial/ethnic and gender differences in depressive symptom levels in early adolescence. • There are racial/ethnic differences in the longitudinal patterns of depressive development from adolescence to early midlife.
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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 | 0.032 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".