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Record W3084149091 · doi:10.1080/07347332.2020.1815926

Body image concerns of young adult cancer survivors: A brief report

2020· article· en· W3084149091 on OpenAlexaff
Madison F. Vani, Catherine M. Sabiston, Anika Petrella, Scott C. Adams, Geoff Eaton, Karine Chalifour, Sheila N. Garland

Bibliographic record

VenueJournal of Psychosocial Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsMemorial University of NewfoundlandUniversity of Toronto
Fundersnot available
KeywordsPsychosocialBivariate analysisDescriptive statisticsPsychologyDistressClinical psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Background The purpose of this study was to describe body image among young adult (YA) cancer survivors and examine relationships between body image and personal, medical, and psychosocial variables. Methods: YAs (n = 522; Mage = 34 ± 6 years) completed an online survey and data were analyzed using descriptive statistics, bivariate correlations, and appropriate tests of mean differences. Results: Higher body image concerns were related to less time since diagnosis, lower post-traumatic growth and social support, greater distress, and a higher number of treatments received (rs = .09 to .42; ps < .05). Body image concerns were higher for those currently on treatment (p < .05). Conclusions: Findings suggest greater attention to YAs’ body image is necessary. Specifically, longitudinal research and the development of strategies dedicated to reducing body image concerns among YAs are needed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.403
Teacher spread0.362 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2020
Admission routes1
Has abstractyes

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Same venueJournal of Psychosocial OncologySame topicChildhood Cancer Survivors' Quality of LifeFrench-language works237,207