Feasibility of implementing a community cardiovascular health promotion program with paramedics and volunteers in a South Asian population
Bibliographic record
Abstract
BACKGROUND: The South Asian population in Canada is growing and has elevated risk of cardiovascular disease and diabetes. This study sought to adapt an evidence-based community risk assessment and health promotion program for a South Asian community with a large proportion of recent immigrants. The aims were to assess the feasibility of implementing this program and also to describe the rates of cardiometabolic risk factors observed in this sample population. METHODS: This was a feasibility study adapting and implementing the Community Paramedicine at Clinic (CP@clinic) program for a South Asian population in an urban Canadian community for 14 months. CP@clinic is a free, drop-in chronic disease prevention and health promotion program implemented by paramedics who provide health assessments, health education, referrals and reports to family doctors. All adults attending the recreation centre and temple where CP@clinic was implemented were eligible. Volunteers provided Hindi, Punjabi and Urdu translation. The primary outcome of feasibility was evaluated using quantitative process measures and a qualitative key informant interview. For the secondary outcome of cardiometabolic risk factor, data were collected through the CP@clinic program risk assessments and descriptively analyzed. RESULTS: There were 26 CP@clinic sessions held and 71 participants, predominantly male (56.3-84.6%) and South Asian (87.3-92.3%). There was limited participation at the recreation centre (n = 19) but CP@clinic was well-attended when relocated to the local Sikh temple (n = 52). Having the volunteer translators was critical to the paramedics being able to collect the full risk factor data and there were some challenges with ensuring enough volunteers were available to staff each session; as a result, there were missing risk factor data for many participants. In the 26 participants with complete or almost complete risk factor data, 46.5% had elevated BP, 42.3% had moderate/high risk of developing diabetes, and 65.4% had an indicator of cardiometabolic disease. CONCLUSION: Implementing CP@clinic in places of worship is a feasible approach to adapting the program for the South Asian population, however having a funded translator in addition to the volunteers would improve the program. Also, there is substantial opportunity for addressing cardiometabolic risk factors in this population using CP@clinic.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".