Current Uses (and Potential Misuses) of Facebook: An Online Survey in Physiotherapy
Bibliographic record
Abstract
PURPOSE: In recent years, the use of social media such as Facebook has become extremely popular and widespread in our society. Among users are health care professionals, who must develop ways to extend their professionalism online. Before issuing formal guidelines, policies, or recommendations to guide online behaviours, there is a need to know to what extent Facebook influences the professional life of physiotherapy professionals. Our goal was to explore knowledge and behaviour that physiotherapists and physical rehabilitation therapists practicing in Quebec have of Facebook. METHOD: We used an empirical cross-sectional online survey design (n=322, response rate 4.5%). RESULTS: The results showed that 84.3% of physiotherapy professionals had a Facebook account. Almost all had colleagues or former colleagues as Facebook friends, 21% had patients as friends, and 27% had employers as friends. More than a third of workplaces had clinic pages with information intended for the public. Regarding workplace Facebook policies, 37.3% said that there was no policy and another 41.6% were not aware whether there was one or not. CONCLUSION: There appears to be a need to establish guidelines regarding the use of social media for physiotherapy professionals to ensure maintenance of professionalism and ethical 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".