The Effects of Information Continuity and Interpersonal Continuity on Physician Services Online: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Web-based medical services have become an effective supplement to traditional services in hospitals and an essential part of medical services. Studies have shown that web-based medical services are useful for shortening the delayed admission time and for enhancing the treatment effect from the service continuity perspective. However, the specific measures that patients and physicians should take to improve service continuity remain unknown. OBJECTIVE: Based on the information richness theory and continuity of care, this study investigates the dynamic effects of information continuity and interpersonal continuity on physician services online. METHODS: Data of 7200 patients with 360 physicians covering complete interaction records were collected from a professional web-based platform in China. Content analysis was performed to recognize matching patients and physicians, and least square regression analysis was performed to obtain all empirical results. RESULTS: Empirical results showed that in the short term, information continuity (including offline experience, medical records, and detailed information) influences physicians' web-based services, and their influences show heterogeneity. Moreover, if a patient's online physician is the same physician who he/she has visited offline, we find that interpersonal continuity is important for service. In the long term, information continuity and interpersonal continuity positively improve service continuity by facilitating repeat purchases. CONCLUSIONS: Overall, our findings not only shed new light on patient behavior online and cross-channel behavior but also provide practical insights into improving the continuity of care in online health communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".