Referral Patterns and Training Needs in Psychiatry among Primary Care Physicians in Canadian Rural/Remote Areas.
Bibliographic record
Abstract
OBJECTIVES: This study examined the referral patterns of rural/remote primary care physicians (PCPs) as well as their needs and interests for further training in child/adolescent mental health. METHODS: Surveys were mailed to Canadian rural/remote PCPs requesting participants' demographic information, training and qualifications, referral patterns, and identification of needs and interests for continuing medical education (CME). RESULTS: PCPs were most likely to refer to mental health programs, and excessive wait times are the most common deterrent. Major reasons for referral were to obtain recommendations regarding medications and assessing non-responsive patients. While PCPs expressed higher levels of confidence in making appropriate referrals, they were much less confident in their knowledge and skills in managing mental health problems. Professional development in child/adolescent psychiatry is a moderate or highly perceived CME need. Overall, attention deficit/hyperactivity disorder (ADHD) was the most commonly chosen topic of interest and CME in the community was preferred, but some regional differences emerged. CONCLUSIONS: PCPs viewed limited community resources and self-identified gaps in skills as barriers to service provision. Professional development in child and adolescent mental health for PCPs by preferred modes appears desired.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".