Addressing Data Challenges in the Planning of Public Health Interventions for Canadian First Nations Populations
Bibliographic record
Abstract
Background: One of the biggest challenges facing health planners is the collection of basic data and statistics required for the planning of public health interventions. This challenge increases when such interventions are meant to serve small sub-populations such as First Nations (FN). In addition to the common issues of data quality and quantity, programme planners in Canada face issues that are specific to the Canadian context, and to small sub-populations such as FN. These issues include (i) identifying and employing effective mechanisms for facilitating FN participation in decision-making processes on matters such as immunization programming that directly affect their lives; (ii) identifying and employing effective mechanisms for ensuring that FN perspectives are reflected in research questions and design, data collection and analysis, and recommendations and reports; and (iii) identifying mechanisms for generating data and for sharing data across institutional and jurisdictional boundaries, while respecting the rights of First Nations' to protect their interests in relation to data generation, sharing and use. Of particular importance is FN emphasis on community data Ownership, Control, Access, and Possession or OCAP. Objectives: To describe the nature of the data challenge in relation to planning for a new immunization programme against Human Papillomavirus (HPV) for FN populations in Canada particularly when modelling the natural history and burden of HPV-related disease, and related impacts on quality of life and health care costs, and to describe methods used for surmounting the challenge. Methods: In this presentation, we will explore strategies for (i) facilitating FN participation in decision-making, (ii) incorporating FN perspectives, (iii) generating and sharing data across jurisdictions, and (iv) overcoming data limitations. Strategies for overcoming data limitations include, for example, participating in federal/provincial/territorial HPV working groups, broadening the literature search to include scientific articles dating as far back as the mid-1980s; seeking expert opinion; assessing the degree to which statistics from local, regional and province/territorial studies and registries reflect the situation in other parts of Canada; and requesting data from FN data custodians. Conclusions: Strengthening working relationships with FN is essential to the development of optimal public health interventions. Long term solutions to data and statistical challenges depend on evolving institutional arrangements, and the development of national electronic surveillance systems. The term First Nationaccording to Indian and Northern Affairs Canada (1999) came into common usage in the 1970s to replace the word 'Indian,' which many found offensive. The term Aboriginalis broader; it includes First Nations people, Mitis and Inuit.
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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 | 0.124 | 0.237 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".