Exploring Peer Support Characteristics for Promoting Physical Activity Among Women Living Beyond a Cancer Diagnosis: A Qualitative Descriptive Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".