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Record W3092205847 · doi:10.2196/14081

Identification of Informed Consent in Patient Videos on Social Media: Prospective Study

2020· article· en· W3092205847 on OpenAlexvenueno aff
Jane O’Sullivan, Cathleen McCarrick, Paul Tierney, Donal B O’Connor, Jack Collins, Robert B. Franklin

Bibliographic record

VenueJMIR Medical Education · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsInformed consentSocial mediaMedicinePsychologyFamily medicineInternet privacyComputer scienceWorld Wide WebAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The American Medical Association Code of Medical Ethics states that any clinical image taken for public education forms part of the patient's records. Hence, a patient's informed consent is required to collect, share, and distribute their image. Patients must be informed of the intended use of the clinical image and the intended audience as part of the informed consent. OBJECTIVE: This paper aimed to determine whether a random selection of instructional videos containing footage of central venous catheter insertion on real patients on YouTube (Google LLC) would mention the presence of informed consent to post the video on social media. METHODS: We performed a prospective evaluation by 2 separate researchers of the first 125 videos on YouTube with the search term "central line insertion." After duplicates were deleted and exclusion criteria applied, 41 videos of patients undergoing central line insertion were searched for reference to patient consent. In the case of videos of indeterminate consent status, the posters were contacted privately through YouTube to clarify the status of consent to both film and disseminate the video on social media. A period of 2 months was provided to respond to initial contact. Furthermore, YouTube was contacted to clarify company policy. The primary outcome was to determine if videos on YouTube were amended to include details of consent at 2 months postcontact. The secondary outcome was a response to the initial email at 2 months. RESULTS: The researchers compiled 143 videos. Of 41 videos that contained footage of patient procedures, 41 were of indeterminate consent status and 23 contained identifiable patient footage. From the 41 posters that were contacted, 3 responded to initial contact and none amended the video to document consent status. Response from YouTube is pending. CONCLUSIONS: There are instructional videos for clinicians on social media that contain footage of patients undergoing medical procedures and do not have any verification of informed consent. While this study investigated a small sample of available videos, the problem appears ubiquitous and should be studied more extensively.

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)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.181
GPT teacher head0.492
Teacher spread0.311 · 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.

Study designQualitative
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

Citations6
Published2020
Admission routes1
Has abstractyes

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