Guidelines: The Do’s, Don’ts and Don’t Knows of Creating Open Educational Resources
Bibliographic record
Abstract
Background: In medical education, there is a growing global demand for Open Educational Resources (OERs). However, OER creators are challenged by a lack of uniform standards. In this guideline, the authors curated the literature on how to produce OERs for medical education with practical guidance on the Do’s, Don’ts and Don’t Knows for OER creation in order to improve the impact and quality of OERs in medical education. Methods: We conducted a rapid literature review by searching OVID MEDLINE, EMBASE, and Cochrane Central database using keywords “open educational resources” and “OER”. The search was supplemented by hand searching the identified articles’ references. We organized included articles by theme and extracted relevant content. Lastly, we developed recommendations via an iterative process of peer review and discussion: evidence-based best practices were designated Do’s and Don’ts while gaps were designated Don’t Knows. We used a consensus process to quantify evidentiary strength. Results: The authors performed full text analysis of 81 eligible studies. A total of 15 Do’s, Don’t, and Don’t Knows guidelines were compiled and presented alongside relevant evidence about OERs. Discussion: OERs can add value for medical educators and their learners, both as tools for expanding teaching opportunities and for promoting medical education scholarship. This summary should guide OER creators in producing high-quality resources and pursuing future research where best practices are lacking.
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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 | 0.056 | 0.244 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.012 | 0.008 |
| Research integrity | 0.020 | 0.015 |
| Insufficient payload (model declined to judge) | 0.031 | 0.022 |
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".