Academic detailing among psychiatrists – feasibility and acceptability
Bibliographic record
Abstract
Purpose Research has shown that academic detailing (AD), which includes repeated in-person educational messages in an interactive format in a physician's office, is among the most effective continuing medical education (CME) forms for improving prescribing practices and reducing drug costs. The purpose of this paper is to investigate AD's feasibility and acceptability as an educational tool among psychiatrists and its ability to facilitate positive changes in antipsychotic prescribing. Design/methodology/approach All psychiatrists practicing in Southwestern Ontario, Canada were invited to participate. Participants (32/299(10.7 percent)) were provided with two educational sessions by a healthcare professional. Participants evaluated their AD visits and completed a pre- and post-AD questionnaire measuring various prescribing practice aspects. Findings A total of 26 out of 32 (81.3 percent) participants completed the post-AD evaluation; most of them (61.5 percent, n=16) felt that AD gave noteworthy information on tools for monitoring side-effects and 50.0 percent ( n=13) endorsed using these in practice. In total, 13 participants (50.0 percent) felt that the AD sessions gave them helpful information on tools for documenting polypharmacy use, which 46.2 percent ( n=12) indicated they would implement in their practice. No significant differences were found between participants' pre- and post-assessment prescribing behaviors. Practical implications There is great need for raising AD program's awareness and improving physician engagement in this process locally, provincially and nationally. Originality/value To the authors' knowledge, this is the first AD program in Canada to target specialists solely. Participant psychiatrists accepted the AD intervention and perceived it as a feasible CME method.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".