Systems that evaluate international equivalency in health-related professions: a scoping review with a focus on Canada
Bibliographic record
Abstract
Health workforce planning has become a significant global problem considering there are estimates of an 18 million healthcare provider shortfall by 2030. There are two mechanisms to address healthcare worker shortages: (1) domestic education of those professions and (2) integration of internationally educated health professionals. Integration of internationally educated health professionals into the Canadian healthcare system requires: (1) reductions in systemic and administrative barriers and (2) development, testing, and implementation of credential equivalency recognition systems. The goal of this scoping review was to identify systems that are employed to determine credential equivalency, with a focus on Canada. The scoping review was carried by employing: (1) a systematic literature search (9) and (2) a website and grey literature Google search of professional governing bodies from a selection of medical/allied healthcare professions, but also other non-medical professions, such as law, engineering and accounting. Seven databases were searched to identify relevant sources: MEDLINE, CINAHL Plus with Full Text, PsycINFO, SPORT Discus, Academic Search Complete, Business Source Complete, and SCOPUS. The search strategy combined keyword, text terms, and medical subject headings (MeSH) and was carried out with the help of a health sciences librarian. Seven articles were included in the final manuscript review from the following professions: nursing; psychology; engineering; pharmacy; and multiple health professions. Twenty-four health-related professional governing body websites were hand searched to determine systems to evaluate international equivalency. There were many systems employed to determine equivalency, but there were no systems that were automated or that employed machine-learning or artificial intelligence to guide the evaluation process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".