Process evaluation of E-learning in continuing medical education: evidence from the China-Gates Foundation Tuberculosis Control Program
Bibliographic record
Abstract
BACKGROUND: E-learning is a growing phenomenon which provides a unique opportunity to address the challenges in continuing medical education (CME). The China-Gates Foundation Tuberculosis (TB) Control Program implemented online training for TB health workers in three provinces of China. We aim to evaluate the implementation of E-learning CME programs, analyse the barriers and facilitators during the implementation process, and to provide policy recommendations. METHODS: Routine monitoring data were collected through the project office from December 2017 to June 2019. In-depth interviews, focus group discussion with project management personnel, teachers, and trainees (n = 78), and staff survey (baseline n = 555, final n = 757) were conducted in selected pilot areas at the provincial, municipal, and county/district levels in the three project provinces (Zhejiang, Jilin, and Ningxia). Descriptive analysis of quantitative data summarized the participation, registration, and certification rates for training activities. Thematic approach was used for qualitative data analysis. RESULTS: By the end of June 2019, the national and provincial remote training platforms had organized 98 synchronous learning activities, with an average of 173.2 people [standard deviation (SD) = 49.8] per online training session, 163.3 people (SD = 41.2) per online case discussion. In the pilot area, 64.5% of TB health workforce registered the asynchronous learning platform, and 50.1% obtained their professional certifications. Participants agreed that E-learning CME was more economical, has better content as well as more flexible work schedules. However, the project still faced challenges in terms of unmet learning needs, disorganized governance, insufficient hardware and software, unsupported environment, and lack of incentive mechanisms. CONCLUSIONS: Our results suggested that it's feasible to conduct large scale E-learning CME activities in the three project provinces of China. Training content and format are key facilitators of the program implementation, while the matching of training supply and demand, organizational coordination, internet technology, motivations, and sustainability are key barriers.
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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 | 0.180 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".