Pharmacist educators in family medicine residency programs: A qualitative analysis
Bibliographic record
Abstract
BACKGROUND: 25-29% of North American family medicine residency programs utilize a pharmacist to teach residents. Little is known about the impact that these pharmacist educators have on residency training. The purpose of this study was to examine the experiences of residents, residency directors and pharmacists within Canadian family medicine residency programs that employ a pharmacist educator to better understand the impact of the role. METHODS: Recruitment from three cohorts (residents, residency directors, pharmacists) within family medicine residency programs across Canada for one-on-one semi-structured interviews followed by thematic analysis of anonymized transcript data. RESULTS: 11 residents, 6 residency directors and 17 pharmacist educators participated in interviews. Data themes were: (1) strong value of the teaching with respect to improved resident knowledge, confidence and patient care delivery; (2) lack of a formal pharmacotherapy curriculum; (3) desire for expansion of pharmacist teaching; (4) impact of teaching on collaboration; (5) impact of teaching on residency program faculty; and (6) lack of criticism of the role. CONCLUSIONS: The pharmacist educator role is valued within residency programs across Canada and the role has a positive impact on several important aspects of family medicine resident training. Suggestions for improvement focused on expanding the teaching role and on implementing a formal curriculum for pharmacist educators to follow.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".