Developing Global Open Access COVID-19 Education for Frontline Healthcare Workers
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
Abstract Background: Early in the Covid-19 pandemic, we identified a heightened need for a reliable, high-quality, accessible, and evidence-based educational resource for frontline healthcare workers. Open access virtual education can reduce disparities in access to education by minimizing cost barriers and providing equitable access to educational content. Our team of global healthcare educators responded by creating an open access competency-based online course to address access disparities around Covid-19 information. The course was aimed toward frontline healthcare workers globally and included design elements such as a built-in language translation tool and non-linear course design to facilitate access and address the individual’s educational needs. Methods: Pre- and post-course surveys were collected to evaluate how the course addressed learner needs. Data were collected between the course launch in April 2020 through December 2020. Results: An initial population of students ( N =149) ranging from high school through doctoral education, living in 23 different countries, speaking 22 different native languages took the course and participated in the pre- and/or post-course surveys. Overall, participants rated the course highly. Conclusion: Open access educational models can facilitate equitable access to education for a global audience.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open science Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it