Social Media Policy for Healthcare Providers in Alberta
Bibliographic record
Abstract
Social media as a form of communication has been widely adopted by much of the world’s population. Social media platforms allow people to connect with others, share ideas and opinions, stay current on world events, and share content such as videos and photographs. For healthcare professionals, social media can also be use to enhance communication with other healthcare professionals, to improve patient care, and to effectively market ones healthcare services. Along with the benefits of social media come risks. Misuse of social media by healthcare providers can pose risks to patients if the confidentiality of health information is not maintained. Unprofessional behaviour by a healthcare professional on social media can also harm the profession to which they belong by undermining the public’s trust in the profession. Finally, misuse can put the professional at risk if their online behavior results in disciplinary action being taken against them by their regulatory college. It is important that regulatory colleges create policies regarding social media use that respects a regulated member’s choice to use social media while decreasing the risks described above. The purpose of this project was to explore the current state of social media policies developed by healthcare colleges for healthcare providers in Alberta. Semi-structured interviews of administrators from various healthcare colleges in Alberta were conducted. The goal of the interviews was to gain insight on college administrators’ attitudes toward social media use, how online misconduct is being handled, and how colleges are adapting to the constant changes in social media. Data gathered from the interviews was analyzed using an inductive thematic approach to identify major themes in the data. The interviews revealed that different healthcare colleges currently take very different approaches to the issue of social media use by their members. The approaches ranged from some colleges having very specific and constantly evolving guidelines for members, to other colleges that have taken no specific actions to address social media issues. The interviews also showed that different colleges are facing very different social media related issues with no two colleges facing the same set of concerns. Lastly, the interview participants shared very common approaches to dealing with misconduct on social media by their members which focused on education to prevent future problems.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".