Measuring Community Strengths – Using Data from The First Nations Regional Health Survey Linked with A Whole-Population Administrative Data Repository
Bibliographic record
Abstract
IntroductionAdministrative data studies routinely report that First Nations mothers and children experience a disproportionate burden of poor health. Due to the nature of administrative data, research often takes a deficits-oriented approach. First Nations health research needs to consider the role that community- and individual-level strengths play in promoting wellbeing and examine how these interact with the delivery and outcomes of health programs. Objectives and ApproachThe First Nations Health and Social Secretariat of Manitoba (FNHSSM) and the University of Manitoba partnered to construct measures of community-level strengths that can be linked with administrative data to examine the delivery and outcomes associated with population health programs delivered in First Nations communities. We linked data from the FNHSSM-administered Regional Health Survey (RHS) with administrative data housed in the Manitoba Population Research Data Repository. We identified 60 questions from the child, youth, and adult versions of the RHS to measure community strengths. We used principal component analysis to identify strength-based constructs. We used Eigen values and percent of variance explained to determine the final number of factors. We used random group resampling and bootstrap methods to test for community-level homogeneity. Community-level factor scores were calculated as the scaled combination of RHS questions within each factor and averaged to the community. ResultsWe identified 12 constructs of community strength: 5 from child responses, 4 from youth, and 3 from adult responses. Strength-based constructs common to all age groups included knowledge of traditional language, involvement in cultural events, and connection with community. Conclusion / ImplicationsColonial approaches to health research perpetuate deficit-based dialogues and negative portrayal of First Nations peoples. First Nations health research should consider how community strengths promote health and interact with program delivery. Including measures of community strength leads to richer understandings of factors that promote wellness among First Nations peoples.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".