Facilitators and Barriers to the implementation of the BETTER WISE intervention: A qualitative study
Bibliographic record
Abstract
Context: The BETTER WISE project involved a comprehensive, evidence-based approach for cancer and chronic disease prevention and screening (CCDPS) that also addressed cancer survivorship (breast, colorectal, prostate) and screened for lifestyle risks and financial difficulty. The intervention was provided by the Prevention Practitioner (PP), a member of the primary care team with enhanced skills in prevention, screening, and cancer survivorship. PPs met with patients 40 to 65 years of age to provide them with an overview of their individual risk for cancer and chronic disease, eligibility for screening, and assistance with lifestyle counseling. Objective: To understand the facilitators and barriers to the implementation of the BETTER WISE intervention. Methods: A qualitative study - Forty-eight key informant interviews and 17 focus groups were conducted with 132 primary care team members (PPs, physicians, allied health professionals, and clinic staff). Written feedback from patients was also collected (585 feedback forms). All data was analyzed using a constant comparative method informed by grounded theory in a first round of coding. The second round of coding employed the Consolidated Framework for Implementation Research (CFIR) to focus analysis on the most salient categories of the five CFIR domains to identify the facilitators and barriers to the implementation of BETTER WISE. Setting: Thirteen primary care settings (urban, rural, and remote) across 3 Canadian Provinces (Alberta, Ontario, and Newfoundland and Labrador). Results: The following key elements were identified within the five CFIR domains: 1) Intervention characteristics – relative advantage and adaptability (in the context of the COVID-19 pandemic); 2) Outer setting – patients’ needs and resources (PPs compensated for increased patient needs and decreased resources); 3) Characteristics of individuals – patients and physicians described PPs as compassionate, knowledgeable, helpful; 4) Inner setting – network and communication (collaboration and support in teams or lack thereof); and 5) Process of implementation – COVID-19 hindered execution, but PPs mitigated and adapted to challenges. Conclusions: Despite the COVID-19 pandemic, the BETTER WISE intervention continued, driven by the PPs and their strong relationships with patients, primary care team members, and the BETTER WISE team. Our learnings may help inform implementation strategies for CCDPS programs facing external challenges.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".