EQUITY-BASED CHILDHOOD IMMUNIZATION POLICY-MAKING IN URBAN PUBLIC HEALTH UNITS ACROSS THE CANADIAN PRAIRIES: A COMPARATIVE STUDY
Bibliographic record
Abstract
Inequities in childhood immunization coverage rates increase the risk of disease outbreaks among vulnerable populations. This study assessed inequities in childhood measles, mumps and rubella (MMR) immunization coverage of four major cities across the Canadian prairies and the public health practices that were deployed to reduce inequities. One-dose by age-two MMR coverage rate inequities-over-time-measurements, and a policy-based inquiry into public health practices between 2009 and 2015 were conducted for each case study city. The results show that there were substantial differences in inequities between the provinces. The Saskatchewan case cities both exhibited low but increasing coverage rates, and large but reducing coverage inequities, over the study period. The Albertan case cities exhibited high coverage rates throughout the study period, with predominantly low inequities, except at a neighborhood-coverage level, in both cities. These results suggest that there are provincial differences in immunization policy and programming practices. For the Saskatchewan cases, geographically-based epidemiology, visual management initiatives, and targeted interventions led to successful public health efforts to reduce coverage inequities. Reminder-based interventions were reported as successful initiatives to increase coverage rates across all cases. Finally, in Alberta, a measles outbreak occurred during the study period, and the subsequent intensive efforts in Calgary differentially reached high-income and high home-ownership neighborhoods. Overall, the study suggests that when public health units detect local MMR coverage inequities and make intentional evidence-based efforts, they can be successful in reducing MMR coverage inequities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".