Quarterly stability of dual-factor mental health profiles among high school students: A latent transition analysis.
Bibliographic record
Abstract
This study investigated the stability of dual-factor mental health (DFMH) profiles over an academic quarter (i.e., 3 months) in a sample of 444 high school students residing in suburban areas of the western United States. Utilizing latent transition analysis, three DFMH profiles were identified, complete mental health, troubled, and high internalizing problems. Students categorized within the complete mental health and high internalizing problems profiles exhibited high levels of stability, with 94% to 96% of individuals remaining within the same profile after a 3-month period. Conversely, 27% of students initially classified within the troubled profile displayed improved mental health outcomes, transitioning to the complete mental health category. These findings underscore the prevalence of stable DFMH profiles among most students over a 3-month period. The observed transition patterns inform the timing and frequency of universal school-based mental health screening practices. In addition, the high stability of students within the high internalizing problems profile highlights the importance of closely monitoring this group's symptoms and implementing targeted school-based interventions to address internalizing distress. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.006 |
| 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 | 0.000 | 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".