Teaching children's mental health to family physicians in rural and under serviced areas.
Bibliographic record
Abstract
OBJECTIVE: To evaluate a curriculum for teaching family physicians (FPs) in rural and underserviced areas about children's mental health, and to evaluate a collaborative model of teaching using child psychiatrists and FPs. METHODS: A child psychiatrist and a rural FP provided training to rural FPs in attention-deficit/hyperactivity disorder (ADHD) and disruptive behaviour disorders (DBDs). Training consisted of a half-day workshop in 11 communities located in southwestern Ontario. Workshops included didactic teaching, observation of standardized videos demonstrating interviewing skills, and interactive discussion. Participants completed pre- and posttraining questionnaires about their confidence in managing these conditions, and completed standardized questionnaires on the effectiveness of the workshop and videos. One month after the training, participants were randomly assigned to receive individual interviews. Three months later 2 questionnaires were mailed to participants for evaluation of their confidence after their training and for evaluation of the impact on their practice. RESULTS: Fifty-six FPs attended the workshops and, of these, 80% completed the study. Family physicians reported improved confidence in their abilities to diagnose and treat ADHD and DBDs after the training. CONCLUSION: Didactic presentations by child psychiatrists and FPs, followed by video examples of interviewing skills, and informal discussions with small groups, was found to be an effective curriculum for teaching rural FPs about children's mental health.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".