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Record W4289355019 · doi:10.1177/23743735221117366

Exploring Patient Perspectives of Body Image Conversations in Primary Care: Understandings, Experiences, and Expectations

2022· article· en· W4289355019 on OpenAlexaff
Ling Yang, Ioana Cezara Ene, Larkin Lamarche

Bibliographic record

VenueJournal of Patient Experience · 2022
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsMcMaster UniversityYork UniversityHamilton Health Sciences
Fundersnot available
KeywordsPrimary careThematic analysisIdeal (ethics)Primary health carePsychologyImage (mathematics)NursingHealth careMedicineQualitative researchFamily medicineSociologyComputer sciencePolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Primary care physicians (PCPs) and patients identified body image conversations to be difficult but necessary. As first points of contact in the healthcare system, PCPs are ideal candidates for addressing body image concerns. Through latent thematic analysis of 12 interviews, this paper explores patient preferences with body image conversations in primary care. We identified challenges that patients faced in sharing body image concerns, expectations they hold for physicians, and suggested potential areas of future research and ways to improve care.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.471

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 head0.043
GPT teacher head0.300
Teacher spread0.257 · 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 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

Citations4
Published2022
Admission routes1
Has abstractyes

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