Epistemic inclusion in the Qanuilirpitaa? Nunavik Inuit health survey: developing an Inuit model and determinants of health and well-being
Bibliographic record
Abstract
OBJECTIVE: At the request of Nunavik Inuit health authorities and organizations, the Qanuilirpitaa? 2017 Nunavik regional health survey included an innovative "community component" alongside youth and adult epidemiological cohort studies. The community component objective was to identify and describe community and culturally relevant concepts and processes that lead to health and well-being. METHODS: A qualitative, community-based research process involving workshops and semi-structured interviews was used to generate a corpus of data on health concepts and processes specific to Inuit communities in Nunavik. Thematic analysis and repeated community validation allowed for the identification of three key dimensions of health salient to Inuit experience and eight community-level health determinants. RESULTS: The health model consists of three linked concepts: ilusirsusiarniq, qanuinngisiarniq, and inuuqatigiitsianiq, which reflect distinct dimensions of Inuit health phenomenology. The determinants community, family, identity, food, land, knowledge, economy, and services were generated through analysis and reflect community-level sources of health and well-being. CONCLUSION: The development of the culturally grounded health models and determinants is an exercise of epistemic inclusivity through which researchers and Indigenous communities may form new and equitable paths of knowledge creation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".