Improving the Management of Patients with Schizophrenia in Primary Care: Assessing Learning Needs as a First Step
Bibliographic record
Abstract
Objective: To assess family physician learning needs related to the care of patients with schizophrenia. Methods: Questionnaires were mailed to all family physicians and general practitioners practising in southern Alberta. Physicians were asked to indicate the number of patients with schizophrenia cared for, their interest in improving the care they provided, their preferred learning methods, and the content they wished to learn. Results: A total of 539 surveys were returned for a return rate of 43.8%. Over half of the physicians (53.5%) indicated that they saw 1 to 2 patients with schizophrenia each month. Almost half (48.5%) indicated they were somewhat or very interested in increasing the care provided. Primary learning needs included increasing their knowledge of psychopharmacologic agents and monitoring and adjusting medications. Lectures and half-day workshops were the preferred learning methods. Conclusion: Our study was helpful in identifying the types of education that physicians wanted as well as the duration of the programming prior to the development of teaching interventions.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".