Qualitative Insights into Family Physicians' Perceptions of Academic Detailing for Rheumatoid Arthritis
Bibliographic record
Abstract
Purpose: The message of a paradigm shift in the treatment of rheumatoid arthritis (RA) to early, aggressive and sustained use of Disease Modifying Anti-Rheumatic Drugs and treating to target remission, has not yet reached all Family Physicians (FPs) in Canada. A promising technique to optimize FP practice is Academic Detailing (AD) which involves visits by trained health care professionals to physicians in their offices, providing evidence-based information on a selected topic. The objective of our study was to understand FPs’ perceptions about the feasibility, acceptability, and utility of AD to provide information about RA management. Methods: We conducted one-on-one semi-structured telephone interviews with twelve consecutive FPs who participated in an AD intervention for RA and who agreed to be interviewed. Interview transcripts were analyzed using a qualitative descriptive approach with inductive content analysis. Results: FPs reported appreciating AD for its educational value, convenience, one-on-one interaction, short duration; subject expert input in content, and practical, evidence-based and focused content. Some FPs identified disadvantages, such as difficulty incorporating AD during work days, lack of dedicated CME time, insufficient time for detailed discussions, lack of time to consult information left behind and standardization of the message delivered. AD was acceptable to most FPs, who reported perceived benefits of the visits, including improved confidence in managing RA, anticipating clinical practice changes and willingness to receive AD in the future. Conclusion: Participating FPs perceived AD as a feasible, acceptable and useful CME technique to receive information about RA management aimed at optimizing care.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".