The effectiveness of E-learning in continuing medical education for tuberculosis health workers: a quasi-experiment from China
Bibliographic record
Abstract
BACKGROUND: Given the context of rapid technological change and COIVD-19 pandemics, E-learning may provide a unique opportunity for addressing the challenges in traditional face-to-face continuing medical education (CME). However, the effectiveness of E-learning in CME interventions remains unclear. This study aims to evaluate whether E-learning training program can improve TB health personnel's knowledge and behaviour in China. METHODS: This study used a convergent mixed method research design to evaluate the impact of E-learning programs for tuberculosis (TB) health workers in terms of knowledge improvement and behaviour change during the China-Gates TB Project (add the time span). Quantitative data was collected by staff surveys (baseline n = 555; final n = 757) and management information systems to measure the demographic characteristics, training participation, and TB knowledge. Difference-in-difference (DID) and multiple linear regression models were employed to capture the effectiveness of knowledge improvement. Qualitative data was collected by interviews (n = 30) and focus group discussions (n = 44) with managers, teachers, and learners to explore their learning experience. RESULTS: Synchronous E-learning improved the knowledge of TB clinicians (average treatment effect, ATE: 7.3 scores/100, P = 0.026). Asynchronous E-learning has a significant impact on knowledge among primary care workers (ATE: 10.9/100, P < 0.001), but not in clinicians or public health physicians. Traditional face-to-face training has no significant impact on all medical staff. Most of the learners (57.3%) agreed that they could apply what they learned to their practice. Qualitative data revealed that high quality content is the key facilitator of the behaviour change, while of learning content difficulty, relevancy, and hardware constraints are key barriers. CONCLUSIONS: The effectiveness of E-learning in CME varies across different types of training formats, organizational environment, and target audience. Although clinicians and primary care workers improved their knowledge by E-learning activities, public health physicians didn't benefit from the interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".