Vaccine Preventable Diseases and Vaccination Policy for Indigenous Populations
Bibliographic record
Abstract
Compared with nonindigenous people, indigenous people in first-world countries have experienced much higher rates of many vaccine preventable diseases. This systematic review of published scientific literature, government reports, and immunization guidelines from Australia, Canada, New Zealand, and the United States compares pre- and postvaccination disease rates and vaccination policy for indigenous people in these four countries. Nationally funded universal vaccination programs are clearly the most effective way of reducing disease in indigenous populations. Most successful have been programs for viral diseases in which strain variations are not important and herd immunity is high, such as measles and hepatitis B. For bacterial infections, strain variations (pneumococcal disease), heavy nasopharyngeal colonization of young infants (pneumococcal and Haemophilus influenzae type b disease), low vaccine effectiveness in adults with a high prevalence of risk factors (polysaccharide pneumococcal vaccine), and waning immunity (pertussis) have been associated with continuing or widening disparities between indigenous and nonindigenous populations. However, universal vaccination programs are not always possible. Geographic targeting of all persons in certain regions with high disease rates has been successful, as has targeting of indigenous populations in regions where they constitute larger proportions of the population. In national programs targeting only indigenous people, it has been difficult to achieve high coverage, particularly in urban areas. Innovative program approaches are particularly needed in these situations.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".