Do online health communities enhance patient–physician relationship? An assessment of the impact of social support and patient empowerment
Bibliographic record
Abstract
The diffusion of the Web 2.0 has made it possible for patients to exchange on online health communities, defined as computer-mediated communities dedicated to health topics, wherein members can build relationships with other members. It is now acknowledged that online health communities provide users not only with medical information but also with social support with no time or geographical boundaries. However, in spite of their considerable interest, there is still a paucity of research as to how online health communities alter the patient-physician relationship. This research aims at filling this gap and examines how online health communities, while providing users with computer-mediated social support and empowerment, impact the patient-physician relationship. Six hypotheses are proposed and tested. A survey was developed and 328 responses were collected from online patient groups in Canada in 2016. The data were analysed using structural equation modelling. All but one hypothesis are validated. The results show that user computer-mediated social support positively influences user empowerment and participation during the consultation, which in turn determines user commitment to the relationship with the physician. Importantly and contrary to our expectations, user empowerment is found to be significantly but negatively related to user commitment with the physician.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".