Validation of a Culturally Appropriate Social Capital Framework to Explore Health Conditions in Canadian First Nations Communities
Bibliographic record
Abstract
An earlier study of our research group formulated a conceptual framework of social capital for First Nation communities and developed a culturally appropriate instrument for its measurement. We tested this instrument further with the Manitoba (Canada) First Nations Regional Health Survey, 2003. Using data from this survey, we investigated the bonding dimension of the social capital conceptual framework, with a total sample of 2,765 First Nations individuals living in 24 Manitoba First Nations communities. Twenty seven Likert-scale survey questions measured aspects of bonding social capital, socially-invested resources, ethos, and networks. Validation analyses included an evaluation of internal consistency, factor analyses to explore how well the items clustered together into the components of the social capital framework, and the ability of the items to discriminate across the communities represented in the sample. Cronbach’s Alpha was computed on the 27 scale items, producing an Alpha of 0.84 indicating high internal consistency. The factor analyses produced five distinct factors with a total explained variance of 54.3%. Lastly, a one-way analysis of variance run by community produced highly significant F-ratios between the groups on all twenty-seven bonding items. The culturally-sensitive items included in the social capital framework were found to be an appropriate tool to measure bonding aspects among Manitoba First Nations communities. Research and policy implications are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".