Adaptation and qualitative evaluation of the BETTER intervention for chronic disease prevention and screening by public health nurses in low income neighbourhoods: views of community residents
Bibliographic record
Abstract
BACKGROUND: The BETTER intervention is an effective comprehensive evidence-based program for chronic disease prevention and screening (CDPS) delivered by trained prevention practitioners (PPs), a new role in primary care. An adapted program, BETTER HEALTH, delivered by public health nurses as PPs for community residents in low income neighbourhoods, was recently shown to be effective in improving CDPS actions. To obtain a nuanced understanding about the CDPS needs of community residents and how the BETTER HEALTH intervention was perceived by residents, we studied how the intervention was adapted to a public health setting then conducted a post-visit qualitative evaluation by community residents through focus groups and interviews. METHODS: We first used the ADAPT-ITT model to adapt BETTER for a public health setting in Ontario, Canada. For the post-PP visit qualitative evaluation, we asked community residents who had received a PP visit, about steps they had taken to improve their physical and mental health and the BETTER HEALTH intervention. For both phases, we conducted focus groups and interviews; transcripts were analyzed using the constant comparative method. RESULTS: Thirty-eight community residents participated in either adaptation (n = 14, 64% female; average age 54 y) or evaluation (n = 24, 83% female; average age 60 y) phases. In both adaptation and evaluation, residents described significant challenges including poverty, social isolation, and daily stress, making chronic disease prevention a lower priority. Adaptation results indicated that residents valued learning about CDPS and would attend a confidential visit with a public health nurse who was viewed as trustworthy. Despite challenges, many recipients of BETTER HEALTH perceived they had achieved at least one personal CDPS goal post PP visit. Residents described key relational aspects of the visit including feeling valued, listened to and being understood by the PP. The PPs also provided practical suggestions to overcome barriers to meeting prevention goals. CONCLUSIONS: Residents living in low income neighbourhoods faced daily stress that reduced their capacity to make preventive lifestyle changes. Key adapted features of BETTER HEALTH such as public health nurses as PPs were highly supported by residents. The intervention was perceived valuable for the community by providing access to disease prevention. TRIAL REGISTRATION: #NCT03052959, 10/02/2017.
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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 | 0.050 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".