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Reliability of YouTube content targeting Arabic-speaking patients with breast cancer regarding post-mastectomy reconstruction.

2024· article· en· W4399118591 on OpenAlexaff
Haifa Alotaibi, Abeer Alsulaimani, Éric Belzile, Nina Morena, Ari N. Meguerditchian

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsMcGill University Health CentreSt Mary's Hospital CentreMcGill University
Fundersnot available
KeywordsMedicineBreast cancerMastectomyArabicBreast reconstructionReliability (semiconductor)CancerOncologyInternal medicine

Abstract

fetched live from OpenAlex

e13626 Background: The dynamic and easily accessible nature of social media platforms, particularly YouTube (YT), has revolutionized the way breast cancer (BC) patients seek and process information. This study aims to evaluate the quality of YT videos addressing post-mastectomy reconstruction (PMR) that target Arabic-speaking women (ASW), a heavily digitally connected segment of society that may experience challenges in accessing mainstream medical information in Arabic Language (AL). Methods: YT was searched incognito, using the terms: "post mastectomy reconstruction" in (AL) in July 2023. Upload date, video length, number of views, poster identity and other relevant information were compiled. Video understandability and actionability were assessed by physician-reviewers (PR) with the Patient Education Materials Assessment Tool for audiovisual materials (PEMAT A/V), while the DISCERN tool appraised quality. Sponsorship presence, patient vs health care professional narratives, and intended audience were documented. Thematic analysis of most discussed topics was performed. Additionally, cluster analysis was employed to categorize the videos based on their quality. Results: Of 109 videos (mean length: 7 mins.), 67% had sponsorship (of which 45% corporate); 92% were information-based (vs experience-based); 82% targeted general population over BC patients; 93.6% were in AL, and 5.5% had translation. Mean PEMAT scores were 61.3% (understandability) and 20% (actionability). Mean DISCERN score was 2.6/5. Only 21% of videos featured women. Predominant themes were awareness (82.6%), appearance, and body image (68.8%). Less prevalent: sexuality and fertility (4.6%). The likelihood that the PR would recommend the video to a patient was low at 43.1%. Cluster analysis showed that high-quality videos had a higher understandability (74%), actionability (40.5%) and greater likelihood of being recommended by a PR (90.5%). Conclusions: YT content on PMR for ASW is easily understandable but lacks actionability and has moderate overall quality. Videos are highly sponsored and questionably representative of patients’ and women’s perspective. Therefore, Improving YT content, especially in AL, is crucial for supporting BC patients exploring PMR. The identified themes and insights into gender representation provide valuable guidance for content creators aiming to improve digital resources for this digitally connected demographic.

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How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchScholarly communication
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.013
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.126
GPT teacher head0.506
Teacher spread0.379 · 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

Labeled directly by 2 models reading the full record.

MetaresearchScholarly communication

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational
DomainEvaluation
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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