Implementing Online Training for “Animal Disease Detectives” in the Asia-Pacific Region: A Focus Group Study
Bibliographic record
Abstract
There is an identified need to strengthen the field epidemiology workforce training in the Asia-Pacific region. In response, the Asia Pacific Consortium of Veterinary Epidemiology (APCOVE) developed an online training program consisting of 36 modules delivered asynchronously and synchronously across 6 months in 2022. We sought to explore the effectiveness of the program based on participant perception and knowledge acquisition. All participants ( n = 139) were invited to participate in focus groups. Framework analysis was conducted using Biggs’ 3P model as a conceptual framework. In total, 93/139 (67%) trainees completed all competencies (36 modules) and 74/139 (53%) trainees participated in one of 12 focus groups. Participants were from the Philippines ( n = 28), Indonesia ( n = 18), Vietnam ( n = 16), Cambodia ( n = 3), Papua New Guinea ( n = 3), Laos ( n = 3), and Timor-Leste ( n = 3). They valued the interactivity of the modules, including online tools, calculators, and knowledge checks. Module content, including case scenarios, was relevant to the region and applicable to participants’ workplaces. Suggestions for improvement included incorporating local face-to-face sessions to complement the online delivery. The median score for the end-of-competency assessment tasks ranged from 42.5 to 45 (out of 50), and the APCOVE online training program provided an effective and scalable framework to ensure access to up-to-date training resources across the Asia-Pacific region. To improve module access and increase engagement, asynchronous online modules are now available and downloadable in six languages. The provision of face-to-face sessions to complement asynchronous online delivery, and engagement from country partners as mentors, will increase effectiveness and sustainability.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".