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Record W4286712087 · doi:10.1123/jsep.2021-0237

Social Support and Body Image in Group Physical Activity Programs for Older Women

2022· article· en· W4286712087 on OpenAlexaff
Michelle C. Patterson, Meghan H. McDonough, Jennifer Hewson, S. Nicole Culos‐Reed, Erica Bennett

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsUniversity of British ColumbiaUniversity of Calgary
Fundersnot available
KeywordsPsychologyGroup (periodic table)Social psychologyImage (mathematics)Developmental psychologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Physical declines with aging may negatively impact women's body image. Group physical activity can be a source of social support that may improve body image. We examined how social support experienced in group physical activity programs impacts older women's body image. Guided by interpretive description, we interviewed 14 women age 65 years and older who participated in group physical activity classes. Although women experienced both positive and negative body image, body image was generally positively impacted by physical activity. Four themes described social support processes that affected body image in the physical activity context: fitting in and being inspired through identifying with others; what is discussed and not discussed; providing comfort, understanding, and acceptance; and skilled and empathetic interactions with instructors. Understanding how social support in group physical activity can promote positive body image throughout aging can inform practical guidelines for facilitating and improving support in this context.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.475
Teacher spread0.426 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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