The Web-Based Physician is Ready to See You: A Nationwide Cross-Sectional Survey of Physicians Using a Mobile Medical App to Evaluate Patients With Sexually Transmitted Diseases in China
Bibliographic record
Abstract
BACKGROUND: Web-based medical service provision is increasingly becoming common. However, it remains unclear how physicians are responding to this trend and how Web-based and offline medical services are linked. OBJECTIVE: The objectives of this study were to examine physicians' use of mobile medical apps for sexually transmitted disease (STD) consultations and identify the physicians who frequently use mobile medical apps to evaluate patients with STD. METHODS: In August 2017, we conducted a nationwide cross-sectional survey among physicians registered on a mobile medical app in China. We collected data on physicians' demographic information, institutional information, and Web-based medical practices. We compared physicians who used mobile medical apps to evaluate patients with STD frequently (at least once a week) with infrequent users. Bivariate and multivariate logistic regressions were used to identify physicians who frequently evaluated patients with STD on mobile medical apps. RESULTS: A total of 501 physicians participated in the survey. Among them, three-quarters were men and the average age was 37.6 (SD 8.2) years. Nearly all physicians (492/501, 98.2%) recommended their last Web-based patient with STD to subsequently see a physician in the clinic. More than half (275/501, 54.9%) of physicians recommended STD testing to Web-based patients, and 43.9% (220/501) provided treatment advice to patients with STD. Of all physicians, 21.6% (108/501) used mobile medical apps to evaluate patients with STD through Web more than once a week. Overall, 85.2% (427/501) physicians conducted follow-up consultation for patients with STD using mobile medical apps. Physicians working at institutions with STD prevention materials were associated with frequent evaluation of patients with STD on mobile medical apps (adjusted odds ratio=2.10, 95% CI 1.18-3.74). CONCLUSIONS: Physicians use mobile medical apps to provide a range of services, including Web-based pre- and posttreatment consultations and linkage to offline clinical services. The high rates of referral to clinics suggest that mobile medical apps are used to promote clinic-seeking, and not replace it. Physicians' use of mobile medical apps could benefit sexual minorities and others who avoid formal clinic-based services.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".