What is a prevention visit? A qualitative study of a structured approach to prevention and screening – the BETTER WISE project
Bibliographic record
Abstract
BACKGROUND: This qualitative study is a sub-component of BETTER WISE, a comprehensive and structured approach that proactively addresses chronic disease prevention, screening, and cancer survivorship, including screening for poverty and addressing lifestyle risks for patients aged 40 to 65. Patients (n = 527) from 13 primary care clinics (urban, rural, and remote) in Alberta, Ontario, and Newfoundland & Labrador, Canada agreed to participate in the study and were invited to a one-hour prevention visit delivered by a Prevention Practitioner (PP) as part of BETTER WISE. We identified the key components of a BETTER WISE prevention visit based on patients' and primary care providers' perspectives. METHODS: Primary care providers (PPs, physicians and their staff) participated in 14 focus groups and 19 key informant interviews to share their perspectives on the BETTER WISE project. Of 527 patients who agreed to participate in the study and were invited for a BETTER WISE prevention visit with a PP, we received 356 patient feedback forms. We also collected field notes and memos and employed thematic analysis using a constant comparative method focusing on the BETTER WISE prevention visit. RESULTS: We identified four key themes related to a BETTER WISE prevention visit: 1) Creating a safe environment and building trust with patients: PPs provided sufficient time and a safe space for patients to share what was important to them, including their concerns related to poverty, alcohol consumption, and mental health, topics that were often not shared with physicians; 2) Providing personalized health education: PPs used the BETTER WISE tools to provide patients with a personalized overview of their health status and eligible screening; 3) Non-judgmental empowering of patients: Instead of directing patients on what to do, PPs evoked patients' preferences and helped them to set goals (if desired); and 4) Integrating care for patients: PPs clarified information from patients' charts and surveys with physicians and helped patients to navigate resources within and outside of the primary care team. CONCLUSIONS: The results of this study underscore the importance of personalized, trusting, non-judgmental, and integrated relationships between primary care providers and patients to effectively address chronic disease prevention, screening, and cancer survivorship as demonstrated by the BETTER WISE prevention visits. TRIAL REGISTRATION: This qualitative study is a sub-component of the BETTER WISE pragmatic, cRCT, trial registration ISRCTN21333761 (date of registration 19/12/2016).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".