Empty Houses, Loose Dogs, and Engaged Citizens: Lessons Learned From Community Participatory Data Collection in Rural Areas
Bibliographic record
Abstract
Rural surveys tend to oversample easy to reach populations, which can misrepresent community health needs. To achieve baseline data reflective of the communities served by the high obesity program (HOP), an innovative evaluation plan was created to prioritize equity and reflect the whole community. Principles of Community-Based Participatory Research (CBPR) were used to guide instrument development, research protocols, and data collection. A random sample of addresses created with United States Postal Service records provided a representative list of addresses in three HOP counties in Louisiana. Local data collectors were recruited to go door to door at the selected addresses to collect in-person surveys (N = 682; response rate of 84%). Over a quarter of participants reported using the charitable food system and walking for transportation at least weekly. Collecting door to door data in rural communities presents unique challenges, including abandoned properties, inaccurate address records, loose dogs, and at times, racial tensions and houses far removed from public roads. Lessons learned include the importance of local knowledge, adapting protocols to fit local conditions, and community awareness of the survey. Health practitioners need confidence when they are making data-based decisions about interventions, and one way to provide this confidence is to collect data from a true cross-section of the community. With a plan and in partnership with community members, a probability sample is feasible to collect in rural communities.
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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 | 0.345 | 0.279 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".