Determining the destination: a co-designed chronic advanced cancer rehabilitation conceptual framework for engagement of individuals with lived experience in rehabilitation research
Bibliographic record
Abstract
BACKGROUND: Individuals living with chronic advanced cancer (CAC) often face distinct physical, functional, and cognitive issues. Their rehabilitation needs are not yet routinely met, warranting further CAC-specific rehabilitation-based research. Given the complexity of functional and symptom presentations, engagement of individuals living with CAC as partners in the research process is encouraged to better understand the lived perspective. Formal engagement requires both structured approaches and iterative processes. The aim was to co-design a conceptual framework to develop and integrate engagement strategies into rehabilitation research focused on CAC populations. METHODS: A multidisciplinary team of authors, including two individuals with lived experience, conducted an implementation-focused descriptive study to inform future research design, including: interviews and follow-up, review of current models and approaches, and development of a co-designed conceptual framework for engaging individuals with lived experience into CAC-specific rehabilitation research. RESULTS: Emergent themes include shared understanding, transparent appreciation, iterative processes and unique partnership needs. A definition, guiding principles and tools for engagement were identified. In consultation with individuals with lived experience, and application of the emergent themes in context, a conceptual framework to guide the engagement process was developed. CONCLUSION: A novel conceptual framework for engaging individuals with lived experience with CAC as partners in rehabilitation research is proposed to facilitate implementation-focused team-based approaches for this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".