Peer Support Physical Activity Interventions Partnering Unknown Survivors of Cancer: A Scoping Review
Bibliographic record
Abstract
Background: Social support is known to facilitate exercise among individuals with cancer; however, this support usually comes from a known source. The use of peer support, from an unknown peer, may facilitate exercise in survivors; however, this has not been well explored in the literature. Purpose: To examine the literature regarding the use, parameters, matching characteristics, and effectiveness of peer support physical activity interventions partnering unknown peers for individuals living beyond a cancer diagnosis. Methods: Six databases were searched for relevant reports up to December 17, 2021. Title/abstract screening, full-text review, and data extraction were completed in duplicate. Data were extracted for information on population, intervention and partner matching characteristics, and study outcomes. A qualitative synthesis was used to summarize findings and descriptive statistics were used to summarize applicable results. Results: Twelve reports were included in this review, describing 6 unique partner-based peer support physical activity interventions. Most interventions (83%) incorporated peers using a mentor/mentee relationship, where one peer acted as a topic “expert,” assisting the other peer around physical activity. All peers were “unknown” prior to the intervention and all interventions described physical activity level as a primary outcome. All articles including results demonstrated that peer support interventions led to significantly higher levels of physical activity post-treatment. Discussion: Promoting social support via unknown peers has potential to improve physical activity behavior in individuals living beyond a cancer diagnosis. Further research should examine the most appropriate mode of partner communication and the overall effectiveness of these interventions using social support as a primary outcome.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".