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Record W4319723815 · doi:10.1188/23.onf.101-114

Exploring Peer Support Characteristics for Promoting Physical Activity Among Women Living Beyond a Cancer Diagnosis: A Qualitative Descriptive Study

2023· article· en· W4319723815 on OpenAlexaff
Catherine M. Sabiston

Bibliographic record

VenueOncology nursing forum · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineSurvivorship curveAffect (linguistics)Quality of life (healthcare)Cancer survivorshipGerontologyPhysical activityPeer supportCancerCancer treatmentSocial supportActivities of daily livingDescriptive statisticsDescriptive researchPhysical therapyNursingPsychotherapistInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To explore women's perceptions of and preferred peer characteristics for peer mentoring to support physical activity promotion. Understanding how women living beyond a cancer diagnosis perceive peers for physical activity may help guide further health behavior mentoring and support practices. PARTICIPANTS & SETTING: 16 English-speaking adult women living beyond a cancer diagnosis. METHODOLOGIC APPROACH: Following a qualitative descriptive approach, four in-person focus groups were conducted and discussions were analyzed using inductive content analysis. FINDINGS: Participants described four considerations for peer matching: (a) personal characteristics, (b) physical activity characteristics, (c) cancer characteristics, and (d) finding a peer. Similarities in age, life phase, location, history of physical activity, type of cancer, severity of cancer, and personality were integral. An online or mobile application and the ability to create multiple partnerships were preferred. IMPLICATIONS FOR NURSING: Understanding methods to promote physical activity is imperative for long-term survivorship outcomes. Nurses in oncology care settings may promote physical activity and social support for women living beyond cancer diagnoses by facilitating optimal peer matches.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.141
GPT teacher head0.426
Teacher spread0.285 · 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 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

Citations6
Published2023
Admission routes1
Has abstractyes

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