Outsourcing practice-based education: The role of industry representatives and implications for clinical expertise
Bibliographic record
Abstract
In an era of significant human and fiscal constraints, hospitals increasingly rely on industry representatives to fill gaps related to practice-based education. Given their dual sales and support functions, the extent to which education and support functions are, or ought to be, fulfilled by industry representatives is unclear. We conducted an interpretive qualitative study at a large, academic medical centre in Ontario, Canada, during 2021-2022, interviewing 36 participants across the organization with direct and varied experiences with industry-delivered education. We found that ongoing fiscal and human resource challenges prompted hospital leaders to outsource practice-based education to industry representatives, which created an expanded role for industry beyond initial product rollouts. Outsourcing, however, generated downstream costs to the organization and undermined the goals of practice-based education. To attract and retain clinicians, participants advocated for re-investment in practice-based education in-house, with a limited and supervised role for industry representatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".