REFLECTIONS ON CANCER REHABILITATION AND SURVIVORSHIP IN CANADA: OBSERVING TEN YEARS OF EVIDENCE INFORMED PRACTICE
Bibliographic record
Abstract
Given the relative novelty of cancer survivorship in Canada, much seminal work in the area has revolved around establishing basic characteristics and program implementation groundwork. Despite commendable efforts and investments, more work is needed to understand needs and characteristics of unique or vulnerable populations. In the early 2000s, a flurry of initiatives to formally establish survivorship practice and research in Canada was undertaken. A groups of clinicians, clinician scientists, survivorship researchers, and survivors collaborating on the development of practice guidelines on survivorship services for adult populations. These were developed under the stewardship of the Cancer Journey Advisory Group of the Canadian Partnership Against Cancer (CPAC) and the Canadian Association of Psychosocial Oncology (CAPO). As the focus of cancer rehabilitation aligns closely with the principles of survivorship care, this suggests the need to more formally integrate rehabilitation specialists in standard survivorship care. Once early national initiatives were underway, cancer control organizations and cancer researchers across the country began to develop strategic policies in an effort to improve and standardize care. There have been some innovative and unique regional programs developed such as physical activity prescriptions, routine screening for distress and rehab for palliative care, which we discuss. Such initiatives were developed in an effort to improve cancer survivorship services and support for survivors anywhere in Canada. Person-centered care is a central element in this body of work, supported by research funding bodies such as the Canadian Institutes of Health Research (CIHR) and the Canadian Cancer Society (CCS). The shifts in patient care in the last decade are influenced by broader changes in understanding health and illness. The dominant model of disease has shifted from a pathological model to a clinical focus on risk factors and medical surveillance. Increasing focus on terms such as ‘risk’, ‘behavior’ and ‘lifestyle’ have come to take on new meanings in survivor care, influenced by disciplines examining human behavior such as psychology and sociology. In the next decade, new ventures in international collaboration would increase knowledge translation of research findings and achieve evidence guided practice across the continents.
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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 | 0.170 | 0.242 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.035 | 0.038 |
| Scholarly communication | 0.034 | 0.014 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.013 | 0.035 |
| Insufficient payload (model declined to judge) | 0.003 | 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".