Perceived parental support in childhood and adolescence as a tool for mental health screening in students: A longitudinal study in the i-Share cohort
Bibliographic record
Abstract
BACKGROUND: Negative events in childhood are associated with increased risk of mental health problems, and evaluation could help identify students at high risk of mental health disorder. However, childhood adversity measures are difficult to implement in routine care. Perceived parental support in childhood and adolescence may be more easily assessed, as it is a rather neutral and non-intrusive question. METHODS: We retrieved students' health data collected from the French i-Share cohort, in a longitudinal population-based study including 4463 students of 18-24 years of age. Students in this cohort completed a self-reported questionnaire about major psychiatric problems at one-year follow-up. RESULTS: Among 4463 participants, 26% reported a major mental health problem-including suicidal behavior (17%), major depression (7%), and severe generalized anxiety disorder (15%). Adjusted logistic regression revealed that a lower level of perceived parental support was significantly associated with higher risk of any mental health problem. Compared to students who reported extremely strong perceived parental support, students who perceived no support had a nearly 4-fold higher risk of mental health problems (aOR 3.80, CI 2.81-5.13). Lower levels of perceived parental support were dose-dependently associated with higher incidences of suicidal behavior, major depression, and severe generalized anxiety disorder. LIMITATIONS: Study limitations included a moderate follow-up response rate, and retrospective self-report questionnaires. CONCLUSION: Perceived parental support was strongly associated with the incidence of mental health problems among college students. If validated, these results suggest that health professionals should consider using this simple marker to improve mental health risk assessment and screening.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".