Professional identity, pivotal moments, and influences: Implications for preceptor development
Bibliographic record
Abstract
INTRODUCTION: Preceptors are critical in training learners and supporting learner professional identity formation (PIF). This manuscript describes pharmacist preceptors' professional identities (PI), pivotal moments and influences that shaped those PIs, and how this impacts their precepting to inform future preceptor development. METHODS: Semi-structured interviews with experienced preceptors from five experiential education programs were transcribed and analyzed. An abductive approach was used for coding, followed by thematic analysis. RESULTS: Twenty-two participants from various settings described their PI as a medication specialist, care provider, safeguard, educator, and/or manager. Six themes were recognized across the interview question data as critical to forming professional identity. These included: common elements among pharmacists' PIs such as being a medication-related problem solver (theme 1) and helping/serving others (theme 2); a connection between preceptor identity and participant precepting practices (theme 3); and the importance of role models (theme 4), practicing autonomy (theme 5) and being treated as a pharmacist (theme 6) in developing the participants' PI. DISCUSSION: These findings suggest that preceptor development could focus on introducing the concept of PIF, build an understanding of the importance of role models and pivotal moments in supporting PIF, and support the development of preceptor identity as a clinician, educator, or teacher. CONCLUSION: Critically, the findings from this analysis suggest that a preceptor's PI can influence how they precept, the types of experiences they facilitate for learners, and the norms and values they model. These findings will inform future preceptor development programs about their learner's 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 | 0.035 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".