Professional identity, pivotal moments, and influences: Implications for preceptor development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it