Building a Pharmacy Preceptor Development Framework for Nurturing Learner Professional Identity Formation
Bibliographic record
Abstract
OBJECTIVE: To explore experiential education (EE) and preceptor development experts' perceptions for priorities for preceptor development aimed at supporting learner professional identity formation (PIF) and to create a framework for preceptor development to inform future preceptor training programs. METHODS: This multicomponent study involved: 1) conducting virtual focus groups with EE and preceptor development experts to explore perceived preceptor development needs, including content and learning outcomes; 2) utilizing a modified nominal group technique to identify priorities for preceptor development; and 3) creating a preceptor development framework for supporting learner PIF. Transcripts were analyzed to identify specific content areas of focus and practical program insights. Main findings from the focus groups and nominal group technique priorities, informed by PIF and preceptor development literature, were used to create the preceptor development framework. RESULTS: Twenty preceptor development experts participated in 4 focus groups. Focus group data yielded 5 overarching program insights and several priority content areas to inform the preceptor development framework. The framework created contains content elements for preceptor learning (ie, reflecting on the preceptors' own PIF journey, using good precepting practices with a PIF lens) and associated preceptor learning outcomes. To support PIF-related learning and preceptor engagement, the framework describes program design elements related to the structure and delivery of programming (ie, reflection, peer discussion, communities of practice, workplace learning). CONCLUSION: This study expands on existing frameworks for preceptor development that focus on competency to guide EE and ensure preceptors are equipped to support and nurture learners in their PIF.
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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 | 0.034 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".