Quality indicators for blogs and podcasts used in medical education: modified Delphi consensus recommendations by an international cohort of health professions educators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Quality assurance concerns about social media platforms used for education have arisen within the medical education community. As more trainees and clinicians use resources such as blogs and podcasts for learning, we aimed to identify quality indicators for these resources. A previous study identified 151 potentially relevant quality indicators for these social media resources. OBJECTIVE: To identify quality markers for blogs and podcasts using an international cohort of health professions educators. METHODS: A self-selected group of 44 health professions educators at the 2014 International Conference on Residency Education participated in a Social Media Summit during which a modified Delphi consensus study was conducted to determine which of the 151 quality indicators met the a priori ≥90% inclusion threshold. RESULTS: Thirteen quality indicators classified into the domains of credibility (n=8), content (n=4) and design (n=1) met the inclusion threshold. CONCLUSIONS: The quality indicators that were identified may serve as a foundation for further research on quality indicators of social media-based medical education resources and prompt discussion of their legitimacy as a form of educational scholarship.
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.
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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it