Best Practices in Bridging Education: Multiple Case Study Evaluation of Postsecondary Bridging Programs for Internationally Educated Health Professionals.
Bibliographic record
Abstract
AIMS: Bridging education for internationally trained professionals has grown in popularity, but little is known about promising practices for bridging education in allied health professions. This paper addresses this gap by examining the expected outcomes of effective bridging programs, the key features that contribute to their effectiveness, challenges faced by bridging programs, and the appropriate role of regulatory colleges, government, employers, and professional associations in bridging education. METHODS: We conducted a mixed-methods multiple case study analysis of seven bridging programs in Ontario, Canada, in five allied health professions: medical laboratory technology, medical radiation technology, diagnostic medical sonography, respiratory therapy, and physical therapy. RESULTS: Effective bridging programs are accessible and flexible in content and format. The key challenges include developing curricula tailored to participants' needs, identifying appropriate format for program delivery, obtaining clinical placements for participants, and achieving financial sustainability. Government, professional, and educational stakeholders should play a central role in bridging education planning and delivery. CONCLUSION: The success of a bridging program relies on two key components-program design and infrastructure. Partnerships with government, professional, and educational stakeholders facilitate the development of good bridging programs.
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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 | 0.032 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".