A Systematic Review and Qualitative Analysis to Determine Quality Indicators forHealth Professions Education Blogs and Podcasts
Bibliographic record
Abstract
BACKGROUND: Historically, trainees in undergraduate and graduate health professions education have relied on secondary resources, such as textbooks and lectures, for core learning activities. Recently, blogs and podcasts have entered into mainstream usage, especially for residents and educators. These low-cost, widely available resources have many characteristics of disruptive innovations and, if they continue to improve in quality, have the potential to reinvigorate health professions education. One potential limitation of further growth in the use of these resources is the lack of information on their quality and effectiveness. OBJECTIVE: To identify quality indicators for secondary resources that are described in the literature, which might be applicable to blogs and podcasts. METHODS: Using a blended research methodology, we performed a systematic literature review using Google Scholar, MEDLINE, Embase, Web of Science, and ERIC to identify quality indicators for secondary resources. A qualitative analysis of these indicators resulted in the organization of this information into themes and subthemes. Expert focus groups were convened to triangulate these findings and ensure that no relevant quality indicators were missed. RESULTS: The literature search identified 4530 abstracts, and quality indicators were extracted from 157 articles. The qualitative analysis produced 3 themes (credibility, content, and design), 13 subthemes, and 151 quality indicators. CONCLUSIONS: The list of quality indicators resulting from our analysis can be used by stakeholders, including learners, educators, academic leaders, and blog/podcast producers. Further studies are being conducted, which will refine the list into a form that is more structured and stratified for use by these stakeholders.
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 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 | 0.152 | 0.346 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.046 | 0.048 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".