Teaching teams to teach: Program evaluation results from an interprofessional faculty development program in academic family medicine
Bibliographic record
Abstract
Introduction: The transformation of primary care into integrated health care teams has resulted in the urgent need for health professional teachers to be prepared to teach interprofessional learners and to contextualize this teaching to team based health care. At the University of Toronto (UT), new physician teachers in the Faculty of Medicine have access to a professional development program (BASICS) designed to prepare clinician teachers for academic medicine. In 2015, St. Michael’s Hospital opened a 6th family medicine academic health centre and welcomed more than 25 new family physicians and health professional educators (HPEs). Recognizing the new cohort of mixed profession educators, a modified version of the BASICS program was created, tailored to this mixed group of teachers, who all have a role in teaching health professional learners in the department.Purpose/Objective: The modified BASICS program was specifically designed to target an interprofessional (IP) audience (physicians and health professional educators (HPEs)) and evaluated with the goal of determining:1- if the BASICS program could be successfully modified for an IP audience2- if learning about teaching together could facilitate the acquisition of participants’ competencies for both collaborative teaching and clinical practice.Methods: Mixed methods were used including:a pre-program participant needs assessmentpre- and post-program questionnaires (to assess knowledge (MCQs), self perceived collaborative competency (HPCCPS), program reflections)session-specific evaluations of each modulequalitative feedback from module teachers (debrief)Results: 13 physicians and 27 HPEs participated. 100% indicated somewhat or very satisfied with the program. Pre-post HPCCPS (Health Professional Collaborative Competency Perception Scale) indicated improvement in self perceived collaborative competency (p <0.0001) and MCQs showed increased attainment of knowledge over the course. 89.7% reported that learning needs were met and 50% felt more prepared for their teaching roles. Facilitators also found that teaching together enhanced their own collaborative competency.Conclusions: This project demonstrated the feasibility of successfully implementing this educational program for an IP team audience, with potential positive impacts on confidence in teaching, collaborative ability and adoption of an enhanced IP lens amongst participants and teachers. These elements are essential to ensure that future health professionals are appropriately trained to participate in and deliver integrated care.Lessons learned: A pre-program needs assessment of participants was important to ensure their learning needs were identified prior to program planning.Careful use of inclusive language by teachers to model IP behaviour (not too physician focused)Limitations: This program was specifically adapted from a faculty development program provided by the Faculty of Medicine at the UT and may not be applicable in other jurisdictions. The modified program was implemented in an integrated primary care family health team teaching clinic which may not be transferable outside of the primary care setting.Suggestions for future research: The learning from this research and the integration of core adult learning principles and IP teaching pedagogy lend themselves for testing this type of program in other health professional training contexts.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".