Disrupting traditional silos and boundary crossing: Interprofessional precepting in a fourth-year pharmacy rotation
Bibliographic record
Abstract
OBJECTIVES: The objectives of this early-stage, pilot study are to explore an interprofessional (IP) precepting model in which the preceptor is from another discipline to the program of training and; to assess the impact of this model and the associated rotation learning activities on the development of the Canadian Interprofessional Health Collaborative (CIHC) IP competencies among fourth-year pharmacy students. METHODS: Six student-preceptor pairs participated in a four-week (160-h) non-direct patient care Advanced Pharmacy Practice Experience. This interprofessional collaboration (IPC) rotation took place in community-based clinics. Five (out of six) students completed a 14-item retrospective post-then-pre questionnaire based on the CIHC IP competencies and all students participated in a focus group. Four (out of six) preceptors took part in either a focus group or semi-structured interview. Questionnaire data were analyzed using descriptive statistics. Transcripts were analyzed inductively using Braun and Clarke's thematic analysis approach. RESULTS: Students reported increased competence across all six IP competency domains, with an average increase of 0.8 points (p < 0.01). The greatest gain was in the Collaborative Leadership (+1.1) domain, while the smallest was in IP Conflict Resolution (+0.4). Three themes were identified from the qualitative data: (1) learning activities and IP competencies, (2) the hidden curriculum, and (3) the preceptor experience. CONCLUSION: By boundary crossing and disrupting traditional silos, this IP precepting model and the associated rotation learning activities supported pharmacy students' development of IP competencies within workplace-based learning; however, the subtle influences of the hidden curriculum and entrenched professional stereotypes remain important considerations. Preceptor development and support (i.e., community of IP precepting practice) is advised to optimize the delivery of IPC rotations. Despite a small sample size, this pilot study lends support to a novel approach to IPE.
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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 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".