Understanding the Interaction of Patient Members of the Online Health Community and Its Impact on the Patient-Physician Relationship
Bibliographic record
Abstract
This research investigates the emerging field of digitalized health and particularly of the virtual healthcare communities. The goal is this research is to study the causal relationships between credibility and attitude towards virtual health communities as well as trust and attitude towards the physician. An online questionnaire was developed and disseminated to patients and users of medical virtual communities. Confirmatory analyses for structural equations were conducted via SPSS and AMOS. Results show that interpersonal trust coming from virtual health communities has a positive relation with credibility and attitude regarding virtual communities. Interpersonal trust has, also, a positive relation with the attitude regarding the doctor. The credibility of the virtual health communities exhibits a positive relation with attitude towards the platform. However, the relation is negative between credibility and attitude regarding the doctor. Finally, the attitude regarding the doctor exhibits a positive relation with trust in the doctor. This study is the first to measure the relationship between credibility, trust and attitude. Moreover, it facilitates better consideration of the role of users of virtual communities of health and doctors, thereby improving the attitude of patients toward doctors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".