Consumers� Gender Difference in Communicating with Doctors on Social Media: Survey Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Social media is becoming more and more important for communication between doctors and consumers. However, little is known about the effect of doctor-consumer communication on consumers’ health information seeking intention. Meanwhile, gender difference in the effect of doctor-consumer communication has not been studied well. OBJECTIVE The purpose of this study is to investigate how doctor-consumer communication on social media affects consumers’ health information seeking intention and whether this association is moderated by gender. METHODS Based on professional-client interaction theory and social role theory, we propose that doctor-consumer communication can be divided into instrumental and affective communication. These two types of communication influence consumers’ health information seeking intention through trust towards doctors. We also argue that the relationship between doctor-consumer communication and trust towards doctors could be moderated by gender. To validate our proposed research model, we employed the survey method and developed corresponding measurement instruments for constructs in our research model. 352 valid answers were collected from consumers who have experience of communicating with doctors on social media. To analyze the data, partial least square was performed. RESULTS Trust towards doctors was found to influence consumers’ health information seeking intention significantly (t=16.881, P<0.001), while both instrumental (t=6.083, P<0.001) and affective communication (t=5.745, P<0.001) between doctors and consumers on social media influence trust towards doctors significantly. Towards the moderation effect of gender, the effect of both instrumental (t=12.87, P<0.001) and affective communication (t=7.3, P<0.001) on trust towards doctors is greater for females than for males. CONCLUSIONS This study not only demonstrates the effect of doctor-consumer communication on social media on consumers’ health information seeking intention, but also uncovers the role of gender in the impact of doctor-consumer communication. To consider the different genders in doctor-consumer communication on social media, we can understand the effect of doctor-consumer communication on social media on consumers’ health information seeking behavior better and deeper.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 0.001 |
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".