Utilizing transformative learning theory to enhance professional identity formation
Bibliographic record
Abstract
Objective: To analyze the impact of a Transformative Learning Theory (TLT)-based toolkit on pharmacy students' self-evaluation of professional identity formation (PIF). Methods: This prospective, interventional cohort study included pre-clinical pharmacy students in a hospital skills-based course. Study participants were included if they completed the Professional Self Identity Questionnaire (PSIQ-9) and Macleod Clark Professional Identity Scale (MCPIS-9) at baseline (week 1), midpoint (week 8), and endpoint (week 15) of the course. The primary outcome was to assess the mean change in PSIQ-9 and MCPIS-9 scores from baseline to endpoint; the outcome was analyzed using the Wilcoxon-Signed Rank Test. Secondary outcomes included assessing the mean difference in questionnaire scores from baseline to midpoint and midpoint to endpoint. Results: Seventy-nine pharmacy students were eligible, with 11 (14%) completing both questionnaires at all time points and 39 (49%) completing them at baseline and midpoint. Comparing baseline and endpoint scores, there was an increase in the PSIQ-9 mean difference for teaching others and a decrease in the MCPIS-9 for feeling ashamed of the profession. No MCPIS-9 differences were found between baseline and midpoint. Three PSIQ-9 questions, communication, using patient records, and teaching others, were significant at baseline and midpoint. Conclusion: The TLT-based toolkit had a minimal impact on students' self-evaluation of PIF based on the PSIQ-9 and MCPIS-9 questionnaires over a 15-week course. Studies with larger sample sizes and longer durations are needed to provide more conclusive results.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".