A critical analysis of the social media policies in Ontario's healthcare system
Bibliographic record
Abstract
Purpose Social media has made a revolutionary change in the relationship between the customers and business or service providers by enabling customers to publish and share feedback and views about product or service quality. This revolutionary change has not been echoed in some healthcare systems. This study analyses the social media policies of healthcare regulatory authorities in Ontario and explores how these policies encourage or discourage healthcare professionals' use of social media for collecting patient stories and understanding patient experience. Design/methodology/approach The study used qualitative content analysis to analyse the policy documents, focusing on the manifest themes in these documents. It used convenient sampling to select 12 organizations, including regulating and licensing bodies and health service delivery organizations in Ontario. The authors collected 24 documents from these organizations, including policies, practice standards and social media learning materials. Findings In Ontario's healthcare system, social media is perceived as a source of risks to the healthcare professions and professionals. Healthcare regulators emphasize that the codes of conduct and professional standards extend to social media. The study found no systematic recognition of patient stories on social media as a source of information on healthcare quality that can be useful for healthcare professionals. Originality/value The study identifies potential unintended consequences of social media policies in the healthcare system and calls for policy and cultural changes to enable the development of safe social media platforms that can facilitate interaction between healthcare providers and patients, when necessary, without the fear of legal consequences or privacy breaches.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".