Can Preparation of Clinical Teachers in IPC Concepts and Competencies Impact Their Approach to Teaching Students in Clinical Practice? A Promising Approach
Bibliographic record
Abstract
The challenge of providing interprofessional (IP) student placements within health agencies can be affected by two factors: patterns of health care agency placements focusing on students in one health professional program setting at a time; and budgets for funding clinical teaching in post-secondary institutions. These challenges can result in uni-professional, practice-based learning rather than learning to work within IP teams. Given the increasing focus on IP teamwork (World Health Organization 2010), the question arises as to whether there is a way clinical teachers can be prepared to work with students for IP teamwork. One strategy involves training clinical teachers with requisite strategies for working in team-based settings. Interprofessional clinical teaching workshops held at Western University, Canada, were started in 2010, and offered annually each fall. The overall workshop goal was to assist teachers in guiding and assessing students for effective collaborative teamwork. This article reports on a post-workshop evaluation of participants’ self-reported clinical teaching and practice from three workshops. Of the 129 workshop participants approached to provide information, only 30 completed the post-workshop assessment (at variable times following the workshop from six months to two years). Of these, 27 reported changes in interprofessional communication and role clarification, as well as in their clinical teaching and practice. While there are limitations to the study because of the low follow-up rate, this approach supports the conclusion that providing clinical teachers with interprofessional education (IPE) related to their clinical teaching can result in reported modifications in their clinical teaching and practice.
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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 | 0.035 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".