The Use of Open-Source Online Course Content for Training in Public Health Emergencies: Mixed Methods Case Study of a COVID-19 Course Series for Health Professionals
Bibliographic record
Abstract
BACKGROUND: The onset of the COVID-19 pandemic generated an urgent need for credible and actionable information to guide public health responses. The massive open-source online course (MOOC) format may be a valuable path for disseminating timely and widely accessible training for health professionals during public health crises; however, the reach and effectiveness of health worker-directed online courses during the pandemic remain largely unexplored. OBJECTIVE: This study investigated the use of an open-source online course series designed to provide critical COVID-19 knowledge to frontline health workers and public health professionals globally. The study investigated how open-source online educational content can be optimized to support knowledge sharing among health professionals in public health emergencies, particularly in resource-limited contexts. METHODS: The study examined global course enrollment patterns (N=2185) and performed in-depth interviews with a purposive subsample of health professionals enrolled in the course series (N=12) to investigate the sharing of online content in pandemic responses. Interviewed learners were from Ethiopia, India, Kenya, Liberia, Malawi, Rwanda, Thailand, Uganda, the United Arab Emirates, and the United States. Inductive analysis and constant comparative methods were used to systematically code data and identify key themes emerging from interview data. RESULTS: The analysis revealed that the online course content helped fill a critical gap in trustworthy COVID-19 information for pandemic responses and was shared through health worker professional and personal networks. Enrollment patterns and qualitative data illustrate how health professionals shared information within their professional networks. While learners shared the knowledge they gained from the course, they expressed a need for contextualized information to more effectively educate others in their networks and in their communities. Due to technological and logistical barriers, participants did not attempt to adapt the content to share with others. CONCLUSIONS: This study illustrates that health professional networks can facilitate the sharing of online open-source health education content; however, to fully leverage potential benefits, additional support is required to facilitate the adaptation of course content to more effectively reach communities globally.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".