Online Bridging Program for new international palliative medicine fellows: development and evaluation
Bibliographic record
Abstract
OBJECTIVES: International medical graduates (IMGs) who pursue additional training in another country may encounter unique challenges that compromise their learning experience. This paper describes the development of an Online Bridging Program in the Division of Palliative Care at the University Health Network Toronto and examines its effectiveness in improving IMGs' readiness for Canadian fellowship training. METHODS: The annual Online Bridging Program was developed to help new IMGs transitioning to Canadian palliative fellowship using Kern's framework for curriculum development. Following a needs assessment, eight online modules with weekly live sessions were developed and underwent external content validation and usability tests. After each iteration, the programme was improved based on participant feedback. Evaluation was conducted first through an online survey immediately on completion of the programme and then through qualitative interviews 6 months into the fellowship. The interviews were analysed using Braun and Clarke's model for thematic analysis. RESULTS: Nine IMGs participated in the Online Bridging Program from 2018 to 2020. All nine participated in the survey and eight in the interviews. Responses to the online survey were almost unanimously positive, suggesting its effectiveness in assisting the IMGs' transitions into fellowship. The interviews revealed four major themes: the importance of combining online modules and live sessions, reducing the fellows' anxiety and easing the transition into their new role, an improved overall learning experience and recognising online format limitations. CONCLUSION: The Online Bridging Program effectively eased IMG palliative medicine fellows' transition into training and enhanced their learning experience.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".