Mixed methods participatory social justice community engagement model
Bibliographic record
Abstract
Context: This is a community engagement model based on a Mixed Methods Participatory Social Justice (MMPSJ) research project. The community engagement model evolved as both synthesis and dissemination were co-created with participants. Indigenous community members alongside researchers and Elders explored health literacy in an effort to illuminate root causes of the social determinants of health (SDoH) and to build community capacity. Objective: To better understand the connections between health and literacy from a local perspective (living on Treaty Six). Design: Mixed methods participatory social justice and community based participatory health research. Participants: There were: 12 participants; ten Indigenous intergenerational family members including an Indigenous Elder and two researchers. Expected Results: Local, contemporary, Indigenous perspectives were shared in ways that were meaningful to participants. Research Questions: In what ways can literacy be considered a social determinant of health from an urban Indigenous community? What literacy issues marginalize the community? How would you like this information shared or disseminated? Conclusions: Appropriate engagement with local community can inform the social determinants of health in an appreciative way, can enhance ethical space, and a richer understanding within community-based research. This capacity building approach will impact health care practitioners, educators, policies, and help to strengthen relations across systems. This research was reviewed and approved by the Behavioural REB at the University of Saskatchewan.
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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 | 0.136 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".