Increasing Response Rates on Face-to-Face Surveys with Indigenous Communities in Canada: Lessons from Pictou Landing
Bibliographic record
Abstract
BACKGROUND: Designing an effective survey for gathering primary health data using a community-based participatory research (CBPR) approach in Indigenous communities in Canada has its challenges. Yet, the Pictou Landing First Nation (PFLN) Native Women's Group (NWG) and academic research partners achieved a 59% response rate. OBJECTIVES: To share lessons learned with both campus and community-based research teams engaged in CBPR involving Indigenous communities on the process of team development, and particularly survey development and execution, as well as the factors that led to a reliable and valid household level environmental health survey that achieved a 59% response rate. METHODS: Multiple debriefings conducted over the course of the 12-month data collection period allowed us to modify our protocol to fit with community oscillations. RESULTS/LESSONS LEARNED: Unique aspects of CBPR allowed for the development of a culturally appropriate survey protocol and culturally relevant variables that reflected the concerns of the NWG, and presenting preliminary data to the community also encouraged community buy-in to participate. CONCLUSIONS: Sharing lessons learned in this project are intended to have positive implications for future CBPR projects wanting to collect primary health survey data involving Indigenous 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 | 0.076 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".