Effects of main components of a targeted prevention program for depressive and anxious symptoms during the transition to higher education
Bibliographic record
Abstract
Introduction The current study assessed the effectiveness of the main components of the targeted prevention (TP) intervention of a multilevel prevention program for depression and anxiety in college students. Objective The TP intervention includes ten ninety-minute workshops for groups of five to ten students who reported significant anxious or depressive symptoms during an initial universal prevention (UP) intervention. Method Participants were 307 students, aged 16 to 30. Measures of intrapersonal risk and resiliency factors, including cognitive distortions, mindfulness principles and practice, managing anxiety, mental illness literacy, and the development of personal goals and values were collected. Latent growth models provided strong support for the effectiveness of the intervention, with a greater reduction in depressive symptoms in the experimental group than the comparison group. Results Although the model was not significant for anxious symptoms, a significant finding in the experimental individual model indicated a sizeable reduction in anxious symptoms from Time 1 to Time 3. Examination of the program determinants revealed significant changes in those that participated in the program compared to the comparison group that did not. Conclusion The beneficial effects of the implementation of multilevel prevention programs in college students and the study limitations are 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".