Facilitators and Barriers to the Implementation of BETTER WISE, a Chronic Disease and Prevention Intervention in Canada: A Qualitative Study
Bibliographic record
Abstract
The aim of the BETTER WISE intervention is to address cancer and chronic disease prevention and screening (CCDPS) and lifestyle risks in patients aged 40-65. The purpose of this qualitative study is to better understand facilitators and barriers to the implementation of the intervention. Patients were invited for a 1-h visit with a prevention practitioner (PP), a member of a primary care team, with specific skills in prevention, screening, and cancer survivorship. We collected and analyzed data from 48 key informant interviews and 17 focus groups conducted with 132 primary care providers and from 585 patient feedback forms. We analyzed all qualitative data using a constant comparative method informed by grounded theory and then employed the Consolidated Framework for Implementation Research (CFIR) in a second round of coding. The following key elements were identified: (1) Intervention characteristics-relative advantage and adaptability; (2) Outer setting-PPs compensating for increased patient needs and decreased resources; (3) Characteristics of individuals-PPs (patients and physicians described PPs as compassionate, knowledgeable, and helpful); (4) Inner setting-network and communication (collaboration and support in teams or lack thereof); and (5) Process-executing the implementation (pandemic-related issues hindered execution, but PPs adapted to challenges). This study identified key elements that facilitated or hindered the implementation of BETTER WISE. Despite the interruption caused by the COVID-19 pandemic, the BETTER WISE intervention continued, driven by the PPs and their strong relationships with their patients, other primary care providers, and the BETTER WISE team.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".