Factors influencing engagement in online dual practice by public hospital doctors in three large cities: A mixed-methods study in China
Bibliographic record
Abstract
Background: In the digital age, a rising number of public sector doctors are providing private telemedicine and telehealth services on online health care platforms. This novel practice pattern - termed online dual practice - may profoundly impact health system performance in both developed and developing countries. This study aims to understand the factors influencing doctors' engagement in online dual practice. Methods: Using a mixed-methods design, this study concurrently collects quantitative demographic and practice data (n = 71 944) and semi-structured interview data (n = 32) on secondary and tertiary public hospital doctors in three large Chinese cities: Beijing, Shanghai and Guangzhou. We use the quantitative data to examine the prevalence of the online dual practice and its associated factors via the binary logit regression model. The qualitative data are used to further explore associated factors of online dual practice via thematic analysis. The findings about associated factors from the two parts were merged using the categories of personal, professional, and organisational characteristics. Results: Our quantitative analysis shows that at least 47.1% of public hospital doctors are involved in online dual practice. The shares in Beijing, Shanghai, and Guangzhou are 43.7%, 53.1%, and 44.8%, respectively. This practice is more prevalent among doctors who are male, senior, and non-managerial. Different specialties, hospital ownership, hospital levels, and locations are also significantly associated with this practice. The qualitative analysis further suggests that financial returns, perceived effectiveness of telemedicine, and hospital directors' attitude towards telemedicine may affect doctors' engagement with online dual practice. Conclusions: Online dual practice is prevalent among doctors at tertiary and secondary public hospitals in Beijing, Shanghai, and Guangzhou. Personal, professional, and organisational characteristics are all associated with doctors' choice to engage in online dual practice. The findings in this study provide implications for promoting telemedicine adoption and developing relevant regulatory policies in China and other countries.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".