The development of a comprehensive maternal–child health information system for Nunavut-Nutaqqavut (Our Children)
Bibliographic record
Abstract
OBJECTIVES: Nunavut is the most northerly jurisdiction in Canada of which 85% of inhabitants are Inuit. Although most infants are born healthy, Nunavut leads the country for adverse early child health outcomes such as infant mortality, rates of birth defects, prematurity and low birth weight. Public health and community efforts are needed to understand and improve outcomes. METHODS: To inform these issues, a combined University of British Columbia/Nunavut Public Health Strategy effort has initiated a comprehensive maternal-child health surveillance system (from 16 weeks gestation to age 5). A diverse group of professional and lay stakeholders were brought together initially to determine local interest. Following this, a series of small working groups were held to decide on potential prenatal, perinatal and early child health variables, to be documented. RESULTS: Over 100 Nunavut participants have now had some role in the development of the system which has been initiated. Pre-existing standard prenatal forms and well-child assessment forms have been modified to include "Nunavut specific" variables of nutrition, food and domestic security, exposures in pregnancy, birth defects, development, chronic diseases of childhood and paternal information. CONCLUSION: This comprehensive maternal-child health information system has been developed with the extensive input of health care providers and stakeholders, utilizing community and public health systems already in place. Careful assessment of local needs has contributed to database development, privacy protection, potential data utilization for health promotion and plans for dissemination of findings. It is hoped that this will be a user-friendly surveillance system, adaptable to other community and public health systems that will improve the understanding of Aboriginal maternal-child health determinants.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".