Pandemic transition to online for healthcare profession education: A webscrape seeking perspectives of innovation and digital equity
Bibliographic record
Abstract
The pandemic caused a sudden and rapid transition to online of health profession education programs, in an attempt to maintain the critical supply of new graduates during a pandemic. A gap existed pre-pandemic between technology mediated pedagogy and digital health literacy; a gap that was forced to narrow. Health education educators considered digital equity for students and the resultant impact of the digital divide in online environments for competency attainment related to digital health literacy and quality patient care. This team engaged in an emancipatory action research webscrape of the immediate pivot period to online in winter 2020 to summarize the expertise being shared over social media platforms or teaching and learning excellence podcasts and blogs. The search criteria for the webscrape covered three areas including changes in 1) healthcare profession education, 2) innovations, and 3) diversity, equity and inclusion. The results, in relation to pre-pandemic reflections, were on the future of education and maintaining innovative momentum found during the pandemic, the future of healthcare and being attuned to patient needs despite virtual care delivery, along with the future society and ensuring students attain digital wisdom. This webscrape speaks to what health profession education values going forward, reducing the digital divide for students and patients.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.005 | 0.008 |
| 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".