Innovative approach for increasing physical activity among breast cancer survivors: protocol for Project MOVE, a quasi-experimental study
Bibliographic record
Abstract
INTRODUCTION: Physical activity is a cost-effective and non-pharmaceutical strategy that can help mitigate the physical and psychological health challenges associated with breast cancer survivorship. However, up to 70% of women breast cancer survivors are not meeting minimum recommended physical activity guidelines. Project MOVE is an innovative approach to increase physical activity among breast cancer survivors through the use of Action Grants, a combination of microgrants (small amounts of money awarded to groups of individuals to support a physical activity initiative) and financial incentives. The purpose of this paper is to describe the rationale and protocol of Project MOVE. METHOD AND ANALYSIS: A quasi-experimental pre-post design will be used. Twelve groups of 8-12 adult women who are breast cancer survivors (N=132) were recruited for the study via face-to-face meetings with breast cancer-related stakeholders, local print and radio media, social media, and pamphlets and posters at community organisations and medical clinics. Each group submitted a microgrant application outlining their proposed physical activity initiative. Successful applicants were determined by a grant review panel and informed of a financial incentive on meeting their physical activity goals. An evaluation of feasibility will be guided by the reach, effectiveness, adoption, implementation, maintenance (RE-AIM) framework and assessed through focus groups, interviews and project-related reports. Physical activity will be assessed through accelerometry and by self-report. Quality of life, motivation to exercise and social connection will also be assessed through self-report. Assessments will occur at baseline, 6 months and 1 year. ETHICS AND DISSEMINATION: Ethical approval was obtained from the University of British Columbia's Behavioural Research Ethics Board (#H14-02502) and has been funded by the Canadian Cancer Society Research Institute (project number #702913). Study findings will be disseminated widely through peer-reviewed publications, academic conferences, local community-based presentations, as well as partner organisations, including the Canadian Cancer Society.
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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 | 0.037 | 0.033 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.093 | 0.020 |
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".