Predicting depression across multiple domains in a 12 year longitudinal investigation of a population sample of children and adolescents.
Bibliographic record
Abstract
The aim of this longitudinal study was to investigate the strength and relative importance of multiple predictors of depression in adolescence and young adults aged 16 to 20 years. Data for this study were drawn from Statistics Canada's National Longitudinal Survey of Children and Youth. Hierarchical regressions were conducted separately by gender in a mixed sample containing biological mothers and other caregivers and in a sample containing exclusively biological mother-child dyads. In both samples, age predicted depression with adolescents reporting more depression symptoms compared to young adults. Girls reported higher depression scores than boys. Anxiety/depression and lower self-esteem predicted depression for boys. Girls' depression was predicted by loss of a parent, higher anxiety/depression, and higher aggression. The biological mother-child sample revealed a stronger effect of maternal depression as a predictor of depression for girls. Lower parental monitoring predicted depression for girls and parental rejection predicted depression for boys and girls. --Leaf ii.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".