Benefits and harms of patient stories on social media from the perspective of healthcare providers and administrators in Ontario
Bibliographic record
Abstract
There has been a growing use of social media by patients to share their healthcare experiences and produce information that can be helpful to other patients seeking healthcare services. These stories can reveal issues in healthcare quality. However, faced with the inherent risks of social media, healthcare providers have been skeptical about the value of these stories, and many healthcare systems have adopted restrictive and protective policies to control the use of social media by healthcare providers. This study explores healthcare providers' and administrators' perspectives on patient stories on social media and whether they can use the stories to evaluate healthcare experiences. Semi-structured interviews (n = 21) were conducted with healthcare providers and administrators, including physicians, nurses, and quality managers in Ontario, Canada, between April 2018 and May 2019. Inductive and data-driven thematic analysis was used to analyze the data. Several barriers prevent healthcare providers from realizing the benefits of social media, including concerns about the quality of patients' feedback, the professional codes of conduct, and the time and effort required to process these stories. The study findings suggest that cultural changes in the healthcare system might be required to foster the use of social media for healthcare quality improvement and enable the development of a safe patient-provider communication environment that facilitates the exchange of constructive feedback between the two parties without the fear of legal consequences, breaches of patient privacy, or violation of professional codes of conduct.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".