Conceptualizing Educational Comparability in Distributed Health Professions Education: A Scoping Review
Bibliographic record
Abstract
PURPOSE: This study aimed to create greater clarity about the current understanding and formulate a model of how educational comparability has been used in the literature to inform practice. METHOD: The authors conducted a literature search of 9 online databases, seeking articles published on comparability in distributed settings in health professions education before August 2021, with an updated search conducted in May 2023. Using a structured scoping review approach, 2 reviewers independently screened articles for eligibility with inclusion criteria and extracted key data. All authors participated in the descriptive analysis of the extracted data. RESULTS: Twenty-four articles published between 1987 and 2021 met the inclusion criteria. Most articles were focused on medical education programs (n = 21) and located in North America (n = 18). The main rationale for discussing comparability was accreditation. These articles did not offer definitions or discussions about what comparability means. The program logic model was used as an organizing framework to synthesize the literature on practices that schools undertake to facilitate and demonstrate comparability in the design (inputs), implementation (activities), and evaluation (outcomes) of distributed education. Inputs include common learning objectives, identical assessment tools and policies, governance models that enable clear communication, and reporting structure that is supported by technological infrastructure. Activities include faculty planning meetings and faculty development training. Outcomes include student experiences and academic performances. CONCLUSIONS: This study demonstrated that a more complex understanding of the dynamics of educational processes and practices is required to better guide the practice of educational comparability within distributed education programs. In addition to highlighting the need to develop an accepted definition of educational comparability, further elucidation of the underlying dynamics among input, activities, and outcomes would help to better determine what drivers should be prioritized when considering educational change with attention to context within distributed education.
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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 | 0.297 | 0.460 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.045 | 0.037 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.020 | 0.031 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".