The impact of the under-reporting of vital events upon epidemiological and demographic measures of the Manitoba Registered Indian population : an exercise in data quality
Bibliographic record
Abstract
In order for the various levels of government, the biomedical research community, and Aboriginal leadership to more carefully assess the needs of the Canadian Aboriginal population they must have an accurate picture of its demographic and epidemiological characteristics. Researchers of Aboriginal health have often used various data sources without a full appreciation of the flaws inherent in the data. This thesis examines the effect of the under-reporting of vital events upon one such data source, namely the Indian Register, and subsequent ramifications for the epidemiological and demographic analysis of the Manitoba Status Indian population. The study compares the magnitude of the problem for the aggregate of six bands from 1979 through 1983 with further differentiation into sex, residential and regional categories. Each of these populations was adjusted for the late- and under-reporting of vital events in order to obtain a coresponding set of population data for comparison purposes. The principal methodologies employed include direct and indirect standardization of mortality rates, life table analysis of mortality, and analysis of fertility and reproduction. These analyses reveal a preponderance of both birth and death reporting problems associated with the off-reserve populations although all populations were affected to some degree. Demographic and epidemiological calculations for all populations were affected to an extent depending upon the magnitude of the reporting problems and the age strata in which they were concentrated. Mortality rates tended to be inflated as a result of reporting problems.
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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 | 0.337 | 0.666 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.019 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".