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Record W2796207914 · doi:10.1108/ijssp-08-2017-0102

Online physicians, offline patients

2018· article· en· W2796207914 on OpenAlexaff
Anson Au

Bibliographic record

VenueInternational Journal of Sociology and Social Policy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInsiderOriginalitySocial mediaIdentity (music)Value (mathematics)Public relationsPerspective (graphical)Professional ethicsPsychologySociologyMedicineSocial psychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to demonstrate how the nature, gravity, and consequences of physician use of social media use surpass professional identity, by bringing to attention the nuanced, potential conflicts between patient-physician interests in current educational policies. Design/methodology/approach Analyzing a case study of a physician publicly posting and commenting on many of his patients’ information, conversations, and medical conditions on social media. Findings Physician social media use carries many issues that concern ethics and the patient, rather than professional identity and the physician. In response, two sets of ethical standards are developed: one that deals with what constitutes impermissible behaviors online, and another that stipulates appropriate punishments for violations of these codes. Originality/value Most medical education policies and the literature have emphasized professional identity- formation with regards to physician use of social media, rather than ethics. Furthermore, no study exists that presents a clear, concrete, insider perspective at physicians’ improper use of social media.

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)0.002
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 score0.617
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.072
GPT teacher head0.467
Teacher spread0.395 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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