Finding a BETTER way: A qualitative study exploring the prevention practitioner intervention to improve chronic disease prevention and screening in family practice
Bibliographic record
Abstract
BACKGROUND: Our randomized controlled trial (The BETTER Trial) found that training a clinician to become a Prevention Practitioner (PP) in family practices improved chronic disease prevention and screening (CDPS). PPs were trained on CDPS and provided prevention prescriptions tailored to participating patients. For this embedded qualitative study, we explored perceptions of this new role to understand the PP intervention. METHODS: We used grounded theory methodology and purposefully sampled participants involved in any capacity with the BETTER Trial. Two physicians and one coordinator in each of two cities (Toronto, Ontario and Edmonton, Alberta) conducted eight individual semi-structured interviews and seven focus groups. We used an interview guide and documented research activities through an audit trail, journals, field notes and memos. We analyzed the data using the constant comparative method throughout open coding followed by theoretical coding. RESULTS: A framework and process involving external and internal practice facilitation using the new role of PP was thought to impact CDPS. The PP facilitated CDPS through on-going relationships with patients and practice team members. Key components included: 1) approaching CDPS in a comprehensive manner, 2) an individualized and personalized approach at multiple levels, 3) integrated continuity that included linking the patients and practices to CPDS resources, and 4) adaptability to different practices and settings. CONCLUSIONS: The BETTER framework and key components are described as impacting CDPS through a process that involved a new role, the PP. The introduction of a novel role of a clinician within the primary care practice with skills in CDPS could appropriately address gaps in prevention and screening.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".