Editorial Processes in Free Open Access Medical Educational (<scp>FOAM</scp>) Resources
Bibliographic record
Abstract
BACKGROUND: Much of the skepticism toward online educational resources (OERs) in emergency medicine (EM) stems from the low barrier to publishing and a perceived lack of editorial rigor. Learners and educators have demonstrated unreliable gestalt ratings of OERs, suggesting a lack of capacity to consistently appraise these resources. The development of tools to guide clinicians and learners in the selection and use of blogs and podcasts is a growing area of interest. Disclosure of editorial process was identified in previous studies as an important quality indicator for OERs. However, little is known about editorial process in online EM resources and whether it can be reliably integrated into a critical appraisal tool. METHODS: Two reviewers assessed 100 top EM and critical care OERs for mention and description of editorial process and academic and nonacademic affiliations. Ninety-two sites were accessible for review. All sites were also contacted to attempt clarification of their editorial process. Inter-rater reliability for mention and description of editorial process was evaluated using Cohen's kappa, and the relationship between academic affiliation and disclosure of editorial process was assessed by odds ratio (OR). RESULTS: Eleven sites mentioned an editorial process, and 10 of these sites included a description. Five of the seven sites that responded to contact also described an editorial process. Inter-rater agreement was excellent for mention (κ = 0.90) and description (κ = 1.00) of editorial process. Eighteen sites listed academic affiliations and 21 sites had nonacademic affiliations. A greater proportion of sites with academic affiliations disclosed their editorial process compared to sites without academic affiliations (OR = 5.3, 95% confidence interval [CI] = 1.3-21.0; difference in proportions of 0.40, 95% CI = 11.6-60.8). CONCLUSIONS: Although transparency is lacking, editorial processes exist among OERs. Inter-rater reliability for disclosure of editorial process is excellent, supporting its use within critical appraisal tools.
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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 | 0.178 | 0.555 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".