The mechanism of word-of-mouth learning on chronic disease patients’ physician choice in online health communities: Latent Dirichlet allocation analyses and cross-sectional study
Bibliographic record
Abstract
Background: Word-of-mouth learning (WOML) plays a substantial role in patients' physician choice behavior. However, there is still a research gap in analyzing the mechanism of WOML on chronic disease patients' physician choice in online health communities (OHCs) considering individual differences. Objective: This study aims to develop a physician choice mechanism research model to reveal the influence of WOML on chronic disease patients' physician choice decision process from external interaction to internal cognition and emotion in OHCs based on social learning theory (SLT). The moderating effects of reasons for consultation and patients' demographic characteristics on the model's relationships were also explored. Methods: Guided by SLT, this study identified the external interaction factors and internal cognitive and emotional factors by analyzing 72,123 patients' online reviews based on a Latent Dirichlet Allocation model and developed the physician choice mechanism research model. The model was validated using structural equation modeling based on an online questionnaire survey of 526 valid Chinese patients with chronic disease. The moderating effect of reasons for medical consultation and demographic characteristics was examined using multi-group analysis. Results: Status capital (SC), decisional capital (DC), and price value (PV)) were the main external interaction factors to initiating chronic disease patients' internal cognition and emotion (perceived convenience (PC), perceived health benefits (PH), and patients' physician choice intention (CI)). PH and PC significantly mediated the relationship between SC, DC, PV, and CI. Reasons for medical consultation, district, and sex significantly moderated the relationships in the model. Conclusions: Considering individual differences, the results of this study advance a comprehensive understanding of how chronic disease patients interact with the environment through WOML to make physician choice decisions. OHCs can recommend suitable physician information to chronic disease patients considering individual differences to match patients' demands and improve service quality.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".