Exploring Cancer Centres for Physical Activity and Sedentary Behaviour Support for Breast Cancer Survivors
Bibliographic record
Abstract
Background: Up to 90% of breast cancer survivors report low levels of physical activity (pa) and spend approximately 70% of the day in sedentary behaviour. Survivors might not be receiving information about the health benefits of pa and the consequences of sedentary behaviour in the context of their cancer. The primary purpose of the present study was to evaluate cancer centres for pa and sedentary behaviour information and infrastructure. A secondary aim was to evaluate the quality of the information that is accessible to breast cancer survivors in cancer centres. Methods: A built-environment scan of the 14 regional cancer centres in Ontario and an evaluation of the text materials about pa available at the cancer centres were completed. Data analyses included descriptive statistics, proportions, and inter-rater reliability. Results: The infrastructure of the cancer centres provided few opportunities for dissemination of information related to pa through signs and printed notices. Televisions were present in all waiting rooms, which could provide a unique opportunity for dissemination of information about pa and sedentary behaviour. Text materials were rated as trustworthy, used some behaviour change techniques (for example, information about the consequences of lack of pa, barrier identification, and setting graded tasks), and were aesthetically pleasing. Conclusions: These findings represent areas for knowledge dissemination both for the centre and for resources that could be further improved.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".