Adapting systematic scoping study methods to identify cancer-specific physical activity opportunities in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Identifying cancer-specific physical activity programs and post-secondary courses targeting students in academic settings (i.e., "real world" opportunities) may promote physical activity behaviors among cancer survivors. Using knowledge synthesis methods such as systematic scoping study methods may facilitate knowledge tool development and guide evidence-based practice to improve knowledge transfer. However, identifying these opportunities poses a challenge as systematic scoping study methods have yet to be applied and adapted to this context. Thus, to extend systematic scoping study methods, the purpose of the current investigation is to describe the adaptation of systematic scoping study methods in the context of cancer-specific "real world" opportunities in Ontario, Canada. METHODS: Systematic scoping study methods were adapted to develop a knowledge tool, which was a credible resource website for researchers, clinicians, and survivors. Three search strategies including Advanced Google Search, targeted website search, and consultations with experts were used to identify eligible (e.g., appropriate for cancer survivors, offered in the community) cancer-specific physical activity programs. Only the targeted website search was used to search post-secondary institutions because they are centralized onto one government website. RESULTS: Fifty-eight programs and 10 post-secondary courses met the eligibility criteria. Relevant data from these opportunities were extracted, charted, synthesized, and uploaded onto the resource website. The most successful search strategy for cancer-specific physical activity programs was the targeted website search followed by Google Advanced Search and consultations with content experts. CONCLUSIONS: Challenges were experienced due to lack of standard reporting among opportunities, bias of potentially relevant records, and changing nature of resulting records. The current investigation demonstrated that systematic scoping study methods can be applied to cancer-specific physical activity programs and post-secondary courses in the context of cancer survivorship in Ontario yielding robust results. The method can be further adapted and updated in future knowledge syntheses in health-related contexts. SYSTEMATIC REVIEW REGISTRATION: The systematic scoping review method protocol has not been registered.
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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 | 0.117 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.025 | 0.048 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".