Wholistic analysis of data from Qanuilirpitaa? , the 2017 Nunavik Health Survey using culturally grounded concepts
Bibliographic record
Abstract
Context: Inuit experience health through language and culture. Being healthy and well requires a balance between Ilusirsusiarniq (‘bodily health’), Qanuinngisiarniq (‘well-being’), and Inuuqatigiitsianiq (‘quality of social relationships’) (IQI). Together, these interconnected concepts represent the foundation of health, as described in the IQI model of health and well-being developed in Nunavik. Previous quantitative studies focusing on health and the social determinants of health (SDoH) of Inuit most often measured health using single outcome indicators available through survey data. To study health wholistically, two research questions guided the present thesis: How can a wholistic assessment of health based in Nunavimmiut knowledge be defined and developed? How does this assessment map onto individual characteristics and community-level social determinants of health (SDoH)? Specific objectives were to 1) Operationalize a wholistic assessment of health and well-being; 2) Validate the assessment with an available survey question on self-rated health; and 3) Explore the relationship between community-level SDoH and the wholistic assessment of health. Methods: Latent Class Analysis (LCA) was employed to operationalize the IQI model of health. Twenty-one indicators corresponding to the foundational concepts of health as defined in the IQI model were selected. Analyses were conducted on a sample of 1196 Nunavimmiut weighted to represent over 7000 Inuit aged 16 years and older. Data were from the Qanuilirpitaa? 2017 Nunavik Health Survey. Results: LCA revealed three health profiles labeled as ‘excellent’, ‘good’ and ‘fair’. Nunavimmiut in the ‘excellent’ health profile (41%) responded very positively to most indicators included in the LCA, while those in the ‘good’ health profile (37%) responded positively to the indicators. On the other hand, Nunavimmiut in the ‘fair’ health profile (22%) reported lower levels of community cohesion, family relationships, and emotional support. Nunavimmiut in the ‘excellent’ and ‘good’ health profiles were more likely to rate their health as excellent/very good/good; to be over 30 years old; to be in a relationship; and to have participated or volunteered in community events. Conclusion: This study grounded quantitative analyses in a locally developed model of health to understand health wholistically among Nunavimmiut. Understanding how wholistic health relates to individual and community-level SDoH can inform frameworks for promoting and supporting regional and local public health interventions, services, and programs
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".