Aboriginal Community-Centered Injury Surveillance: A Community-Based Participatory Process Evaluation
Bibliographic record
Abstract
While injuries are a leading health concern for Aboriginal populations, injury rates and types vary substantially across bands. The uniqueness of Aboriginal communities highlights the importance of collecting community-level injury surveillance data to assist with identifying local injury patterns, setting priorities for action and evaluating programs. Secwepemc First Nations communities in British Columbia, Canada, implemented the Injury Surveillance Project using the Aboriginal Community-Centered Injury Surveillance System. This paper presents findings from a community-based participatory process evaluation of the Injury Surveillance Project. Qualitative data collection methods were informed by OCAP (Ownership, Control, Access, and Possession) principles and included focus groups, interviews and document review. Results focused on lessons learned through the planning, implementation and management of the Injury Surveillance Project identifying lessons related to: project leadership and staff, training, project funding, initial project outcomes, and community readiness. Key findings included the central importance of a community-based and paced approach guided by OCAP principles, the key role of leadership and project champions, and the strongly collaborative relationships between the project communities. Findings may assist with successful implementation of community-based health surveillance in other settings and with other health issues and illustrate another path to self-determination for Aboriginal communities. The evaluation methods represent an example of a collaborative community-driven approach guided by OCAP principles necessary for work with Aboriginal 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 | 0.160 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".