What are important factors for physical activity peer-partners among women with cancer?
Bibliographic record
Abstract
Engagement in physical activity (PA) and long-term PA maintenance can be challenging for women living beyond a cancer diagnosis. Social support literature suggests that peer-partners may provide various forms of social support as a mechanism for initiating and maintaining PA. The purpose of this study was to assess what women diagnosed with cancer were seeking from a PA partner through ActiveMatch.ca, a platform designed to help women with cancer find an exercise partner. Participants (N = 199, Mage = 50.06 years) reported their reasons for wanting an exercise partner through an open-ended question when registering for the website. Using an inductive content analysis, seven categories were identified and coded within higher-order themes related to processes and outcomes. Two process categories included an understanding that women wanted to develop social support (i.e., desire to have a partner from whom to provide/receive support) and motivation (i.e., desire to have a partner to provide reason/drive for PA). Five outcomes categories included: provide motivation for others (i.e., impact partner’s life and help them through PA), shared accountability/adherence/commitment (i.e., help with regular PA participation, activity maintenance, and goal setting), health (i.e., help improve one’s physical and/or mental health, or to return to ‘normal’), weight loss (i.e., help one lose weight), and increase PA (i.e., help increase activity). These findings can inform the ways researchers design peer interventions, create peer-partner matches, and facilitate social support to improve PA engagement and maintenance for women living beyond a cancer diagnosis.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".