Understanding how to reach the hard to reach in cancer rehabilitation
Bibliographic record
Abstract
Introduction: Regular exercise helps manage side effects of cancer treatment, however, less than 30% of survivors participate in regular exercise. Exercise-related barriers, facilitators, and needs of general populations of cancer survivors are described in the literature. No information exists describing this information for hard to reach populations. Purpose: To determine the barriers, facilitators, and exercise needs of hard to reach cancer survivors. Materials and Methods: Research design: Descriptive qualitative study. Population: Hard to reach cancer survivors, including young adults (18-39 years), those living in rural communities, and those living in areas of low socioeconomic status. Data collection: Semi-structured interviews were conducted with participants. Interviews were audio recorded and transcribed verbatim. Transcripts were coded independently by two researchers. Coded data was aggregated into nodes and grouped into themes. Results: Five themes were identified that influence exercise participation in hard to reach survivors: accessibility of exercise programs, appropriateness of exercise programs, social support, personal factors, and exercise information. Young adults described a lack of appropriate exercise programs for their age group, those in rural settings described availability issues, and those in areas of low SES described cost and social support as barriers to exercise. Conclusion: This project identified unique exercise-related barriers, facilitators, and needs of hard to reach cancer survivors. Results can be used by researchers and clinicians when creating exercise interventions for cancer survivors. Interventions must be tailored to the specific needs of each individual in order to facilitate accessible participation in regular exercise and facilitate sustained behaviour change.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".