Developing Global Open Access COVID-19 Education for Frontline Healthcare Workers
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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How this classification was reachedexpand
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 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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".