Leisure physical activity in people with advanced cancer: a scoping review protocol
Bibliographic record
Abstract
Background: With the increasing survival rates associated with advanced cancer, more people are living with the substantial effects of this health condition and its treatments. Despite reported difficulties in daily activities and potential benefits with leisure physical activity (PA), there is limited knowledge on participation in leisure PA in people with advanced cancer.Objective: The objective of our scoping review is to examine and map the literature on leisure PA in people with advanced cancer. Specifically, we wish to: (a) examine the extent, range and nature of literature on leisure PA in people with advanced cancer; (b) report on how leisure PA has been explored in the identified body of literature and (c) report on the use and definitions of terms to describe the advanced cancer population in the identified body of literature.Methods: The following steps will be taken to perform a scoping review on leisure PA in people with advanced cancer. A peer-reviewed literature search of 11 electronic databases and supplementary material will be conducted. Two reviewers will independently scan titles and abstracts and subsequently review full texts to determine eligibility according to the article selection criteria. Relevant data will be extracted from the included studies by two reviewers. A narrative summary of the findings will be presented with a descriptive analysis of the evidence base and a thematic analysis of content-specific information.Conclusion: This scoping review will provide a comprehensive understanding of the current literature on leisure PA in people with advanced cancer and will identify gaps in knowledge on this topic to guide future research inquiries.
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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 | 0.096 | 0.076 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.066 | 0.015 |
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".