How Preceptors Support Pharmacy Learner Professional Identity Formation
Bibliographic record
Abstract
OBJECTIVE: Pharmacy preceptors play a role in helping learners form professional identities during experiential education. However, it is not clear what specific roles and precepting strategies best foster professional identity formation (PIF). The objective of this study was to explore how preceptors support pharmacy learner PIF. METHODS: This qualitative study used an interpretative descriptive approach. Preceptors from 5 experiential education programs were recruited using purposive sampling for individual semistructured interviews. Interviews were recorded, transcribed, coded, and analyzed by thematic analysis. Team members used a reflective and iterative approach for data analysis and generation of themes. RESULTS: A total of 22 participants were interviewed from various pharmacy practice settings and precept a range of learners, including introductory pharmacy practice experiences, advanced pharmacy practice experiences, and residents. Four main themes were identified to support pharmacy leaner PIF: making learners part of the practice and team, preparing learners to assume the role of a pharmacist, helping learners navigate emotions during practice experiences, and supporting learners in finding the right fit within the profession. Specific precepting strategies associated with each theme were identified. CONCLUSION: Preceptors play an important role in supporting learners in thinking and acting as professionals while also helping navigate emotional experiences that may impact PIF and having conversations to help define learner's future aspirations of the pharmacist they want to become. Strategies identified can inform curricular approaches and preceptor development that intentionally supports 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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".