Neophyte facilitator experiences of interprofessional education: implications for faculty development
Bibliographic record
Abstract
The facilitation of learners from different professional groups requires a range of interprofessional knowledge and skills (e.g. an understanding of possible sources of tension between professions) in addition to those that are more generic, such as how to manage a small group of learners. The development and delivery of interprofessional education (IPE) programs tends to rely on a small cohort of facilitators who have typically gained expertise through 'hands-on' involvement in facilitating IPE and through mentorship from more experienced colleagues. To avoid burn-out and to meet a growing demand for IPE, a larger number of facilitators are needed. However, empirical evidence regarding effective approaches to prepare for this type of work is limited. This article draws on data from a multiple case study of four IPE programs based in an urban setting in North America with a sample of neophyte facilitators and provides insight into their perceptions and experiences in preparing for and delivering IPE. Forty-one semi-structured interviews were conducted before (n = 20) and after (n = 21) program delivery with 21 facilitators. Findings indicated that despite participating in a three-fold faculty development strategy designed to support them in their IPE facilitation work, many felt unprepared and continued to have a poor conceptual understanding of core IPE and interprofessional collaboration principles, resulting in problematic implications (e.g. 'missed teachable moments') within their IPE programs. Findings from this study are discussed in relation to the IPE, faculty development and wider educational literature before implications are offered for the future delivery of interprofessional faculty development activities.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".