Winnipeg's North End Wellbeing Measure: Using Social Innovation to Drive Community Measurements
Bibliographic record
Abstract
BACKGROUND: The Winnipeg Boldness Project, a social innovation initiative addressing early childhood outcomes in the underserved community of Point Douglas, worked alongside the community to develop a meaningful measurement tool, the North End Wellbeing Measure (NEWM). This article describes the context, the research and pilot, and the lessons learned. OBJECTIVES: To develop a community-based tool called the NEWM, which evaluates what is important to Point Douglas families. METHODS: We used community-based participatory research methods and surveys for data collection. LESSONS LEARNED: We learned that 1) the language used in relation to notions of well-being and satisfaction could be more precise, 2) our assumptions about strengths-based measurement did not always align with community perspectives, 3) hiring Indigenous people as data collectors is essential, and 4) we need to remain vigilant in our attention to respecting the participants' lived experiences. We also learned that, given the opportunity, the community has a desire to participate in research involving their experiences and well-being and greatly benefit from self-voicing and agency in research development. CONCLUSIONS: The pilot NEWM demonstrates the benefits and challenges of Indigenous social innovation and will benefit future iterations of the measure, as well as other community-based well-being measures.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| 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".