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Record W4386084281 · doi:10.1080/07347332.2023.2249879

Body image interventions within breast cancer care: A systematic review and concept analysis

2023· review· en· W4386084281 on OpenAlexaff
Lunie Anne Thamar Louis, Justine Fortin, Carol-Anne Roy, Alain Brunet, Annie Aimé

Bibliographic record

VenueJournal of Psychosocial Oncology · 2023
Typereview
Languageen
FieldPsychology
TopicBody Image and Dysmorphia Studies
Canadian institutionsUniversité du Québec en OutaouaisUniversité du Québec à MontréalMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsPsychosocialPsychological interventionOperationalizationBreast cancerSystematic reviewMedicineClinical psychologyPsychologyMEDLINECancerPsychotherapistPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

There needs to be a consensus regarding the definition of body image in oncology the literature. This lack of agreement leads to conflicting results in psychosocial interventions aimed to improve body image among breast cancer patients. Through an instrumentalist approach, this systematic review aims to analyze how body image as a concept is described and operationalized in breast cancer studies with the focus to enhance body image through psychosocial interventions. Databases were searched in October 2022 and updated in February 2023 to find empirical studies reporting psychosocial intervention targeting body image efficacy. The results from 24 studies show many similarities and differences between the definitions (e.g. characteristics) and questionnaires (e.g. Cronbach's alpha coefficient) used to evaluate this concept. Most definitions include thoughts, feelings, and behaviors related to body image. Finally, the psychosocial implications are discussed. This systematic review is registered on the International Prospective Register of Systematic Reviews (PROSPERO; CRD42022326393).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.379
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0090.004
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.069
GPT teacher head0.485
Teacher spread0.415 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations7
Published2023
Admission routes1
Has abstractyes

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