Barriers and Enablers to the Production of Open Access Medical Education Platforms: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Free Open Access Medical Education has the potential to democratize access to medical knowledge globally; however, this potential remains largely unrealized, particularly in resource-limited settings. Content is increasingly concentrated on a small number of platforms, each hosting large volumes of material compiled from diverse sources. OBJECTIVE: This scoping review aimed to identify and synthesize reported barriers and enablers to the successful design, production, and operation of open access medical education platforms, with the goal of informing strategies to improve their impact, reach, and sustainability. METHODS: We conducted a scoping review using the Arksey and O'Malley framework. A structured search was carried out on April 17, 2023, in PubMed and EBSCOhost. Citation chaining with the SnowGlobe tool and manual reference checking supplemented the search. Studies were eligible for inclusion if they examined platforms that compile content from multiple sources and reported barriers and enablers. Two reviewers (AAEA and AB) independently screened records and extracted data, with discrepancies resolved by a third reviewer (EPOF). Beginning with an a priori framework of "barriers" and "enablers," coding was then developed inductively. Thematic synthesis categorized findings by stakeholder group. RESULTS: Of 1108 records identified, 1064 unique records were screened, and 64 full-text papers were assessed; 34 met the inclusion criteria. The most frequently reported barriers were concerns about content-quality control, incomplete or unstructured materials, and the resources needed to sustain platforms long-term. Key enablers included the use of validated tools to assess content quality and collaboration with existing content providers and platforms to enhance visibility and learner engagement. Findings were organized into 3 stakeholder groups: learners and training programs, content designers and creators, and platform managers. CONCLUSIONS: Open access medical education platforms have significant untapped potential to enhance global medical training. Addressing these persistent challenges-particularly around quality assurance, content organization, and sustainability-will require more structured, collaborative, and internationally coordinated approaches.
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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 | 0.098 | 0.351 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.031 | 0.034 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".