Does integration matter? an international cross-sectional study on the relationship between perceived public health and primary care integration and COVID-19 vaccination rates
Bibliographic record
Abstract
BACKGROUND: Immunisation against COVID-19 is crucial for controlling the pandemic, yet global challenges persist in vaccine coverage and equitable distribution. A well-integrated primary health care approach can enhance vaccination programmes. AIM: To explore the relationship between perceived PC (primary care)-PH (public health) integration, as well as other vaccination program implementation factors, and national COVID-19 vaccination coverage. DESIGN AND SETTING: A convenience sample of self-identified primary care professionals completed an online survey on COVID-19 vaccination programme implementation and their perceptions of PC-PH integration. METHODS: Countries with ≥5 responses were included in the data analysis. COVID-19 vaccination implementation approach and perceived PC-PH integration against COVID-19 vaccination coverage was investigated using bivariate and subgroup analyses, Spearman correlation, and linear regression. RESULTS: A total of 394 responses from 32 countries were analysed. Participants included primary care providers, academics, and researchers. The median national COVID-19 vaccination coverage was 28.41% at time of study. Perceived barriers included patient hesitancy and vaccine supply shortages, while facilitators included vaccine product choices, equity, and community engagement. The study revealed a positive relationship between perceptions of PC-PH integration and national vaccination coverage in upper-middle and lower-middle income countries. CONCLUSION: Perceived PC-PH integration increased with decreasing economic quartiles and this perception was linked to actual national vaccination coverage. Integration may be especially important for countries with lesser vaccine supply. High-income countries may benefit from increased collaboration between PC and PH to enhance vaccination efficiency. The findings contribute to understanding the role of PC-PH integration in vaccination programmes in different settings.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".