Adolescent care: Part 1: Are family physicians caring for adolescents’ mental\n health?
Bibliographic record
Abstract
OBJECTIVE: To investigate how often family physicians see adolescents with mental health problems and how they manage these problems. DESIGN: Mailed survey completed anonymously. SETTING: Province of Quebec. PARTICIPANTS: All 358 French-speaking family physicians who practise primarily in local community health centres (CLSCs), including physicians working in CLSC youth clinics, and 749 French-speaking practitioners randomly selected from private practice. MAIN OUTCOME MEASURES: Frequency with which physicians saw adolescents with mental health problems, such as depression, suicidal thoughts, behavioural disorders, substance abuse, attempted suicide, or suicide, during the last year or since they started practice. RESULTS: Response rate was 70%. Most physicians reported having seen adolescents with mental health problems during the last year. About 10% of practitioners not working in youth clinics reported seeing adolescents with these disorders at least weekly. Anxiety was the most frequently seen problem. A greater proportion of physicians working in youth clinics reported often seeing adolescents for all the mental health problems examined in this study. Between 8% and 33% of general practitioners not working in youth clinics said they had not seen any adolescents with depression, behavioural disorders, or substance abuse. More than 80% of physicians had seen adolescents who had attempted suicide, and close to 30% had had adolescent patients who committed suicide. CONCLUSION: Family physicians play a role in adolescent mental health care. The prevalence of mental health problems seems higher among adolescents who attend youth clinics. Given the high prevalence of these problems during adolescence, we suggest on the basis of our results that screening for these disorders in primary care could be improved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".