Integrated Knowledge Translation To Inform Implementation Of Exercise Counselling And Referral Of Cancer Survivors
Bibliographic record
Abstract
There is limited evidence supporting successful implementation of exercise-programming for cancer survivors into cancer clinical care pathways. We designed and launched a five-year hybrid effectiveness and implementation study to evaluate the relative benefit from an Alberta wide clinic-to-community based cancer and exercise model of care - the Alberta Cancer Exercise (ACE) program, and to evaluate the implementation of ACE into clinical cancer care. PURPOSE: To determine Health Care Provider (HCP) preferences, barriers and facilitators towards exercise counselling and referral of survivors to ACE at the Cross Cancer Institute (CCI), Edmonton, Alberta, and to test the feasibility of in-clinic, HCP-informed implementation tools. METHODS: Stage I: A theory-informed electronic questionnaire was distributed to HCPs at the CCI, of which N=47 responded (Aug-Oct 2017). A subsequent focus group N= 7 (May 2018) of CCI HCPs was held to probe into questionnaire findings and to determine actionable strategies. Stage II: Responses were mapped to the Capability Opportunity Motivation Behavior model. Tools were developed to specifically target the needs of HCPs in the head and neck cancer (HNC) tumor group. Tool packages were distributed to HCPs (N=9) for in-clinic use for 4 weeks, corresponding to ACE recruitment for Spring programming (March-April 2019). Referral of HNC survivors to ACE programming was tracked. RESULTS: Stage I: Across all disciplines, only 17% of HCPs reported performing exercise counselling with survivors. The most common HCP identified barrier to exercise counselling was time, followed by a lack of knowledge regarding appropriate exercise. The most common facilitator was the ‘interdisciplinary team’, including access to physical therapy services. Stage II: Tool-based implementation strategies were developed and involved an educational package and exercise screening algorithm that was distributed to HCPs. A total of N=14 HNC survivors were referred, representing more than double the average number of previous HNC referrals (N=6) per session. HCPs reported the implementation tools to be ‘somewhat’ to ‘very helpful’. CONCLUSIONS: HCP-identified implementation tools can enhance exercise-counselling and referral practices, and improve referral to community-based exercise programming.
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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 | 0.039 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".