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Record W2261297252 · doi:10.3138/ptc.2014-41

Current Uses (and Potential Misuses) of Facebook: An Online Survey in Physiotherapy

2016· article· en· W2261297252 on OpenAlexaffvenueabout
Maude Laliberté, Camille Beaulieu-Poulin, Alexandre Campeau Larrivée, Maude Charbonneau, Émilie Samson, Debbie Ehrmann Feldman

Bibliographic record

VenuePhysiotherapy Canada · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsSocial mediaHealth professionalsRehabilitationPsychologyHealth careNursingMedical educationMedicinePolitical sciencePhysical therapy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.125
GPT teacher head0.432
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2016
Admission routes3
Has abstractyes

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