Implementing the Personalised Exercise Rehabilitation in Cancer Survivorship (PERCS) Triage and Referral System in Ireland: Protocol for a Qualitative Stakeholder Study
Bibliographic record
Abstract
Background Exercise rehabilitation offers substantial benefits for people living with and beyond cancer, improving physical, psychological, and disease-related outcomes. Despite strong evidence and policy support, integration of exercise into routine cancer care in Ireland remains limited. The PERCS (Personalised Exercise Rehabilitation in Cancer Survivorship) triage and referral system was developed to provide a structured, stepped-care model directing patients to the appropriate level of exercise support. Following on from a feasibility study, this protocol describes a qualitative study to inform national implementation. Methods This multi-stakeholder study will use focus groups and interviews with four stakeholder groups: 1) Healthcare professionals, 2) Exercise professionals, 3) Policymakers, charity partners, and cancer centre managers, and 4) People living with and beyond cancer, carers and family members. Topic guides, informed by the Consolidated Framework for Implementation Research (CFIR) and tailored to each stakeholder group, will seek to explore barriers, facilitators, and contextual factors that influence implementation. Data will be analysed using framework analysis. Transcripts will be coded using both a CFIR-based deductive coding approach and inductive codes that are relevant to the research aim, and key points will be organised to allow comparison of responses across participant groups. The findings stemming from this work will inform the selection of tailored implementation strategies mapped to the Expert Recommendations for Implementing Change (ERIC) taxonomy. Conclusion This study will generate detailed, context-sensitive evidence on the barriers and facilitators to implementing the PERCS system at a national level. By engaging with key stakeholders and using a structured implementation science approach, the findings will guide the development of practical, policy-relevant strategies to support the integration of exercise rehabilitation into survivorship care. The study will contribute to ongoing efforts to improve quality of life for cancer survivors in Ireland and build capacity for sustainable, person-centred cancer rehabilitation services.
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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 | 0.074 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.063 | 0.011 |
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".