Strategies and challenges for immunisation of children under 5 years in primary health care in different countries: a scoping review
Bibliographic record
Abstract
This study mapped the strategies used by PHC to immunise children under 5 years of age, as well as identifying challenges in achieving vaccine coverage in accordance with goals set by countries' national immunisation programmes. This scoping review followed PRISMA-Scr (Scoping review) recommendations. The databases searched for information were PubMed, Science Direct, Embase and VHL (SciELO), from which 352 publications were retrieved and appraised for eligibility. Ultimately, 66 articles were selected for review. The results point to a variety of vaccination strategies used in different countries. Challenges facing PHC in improving vaccination coverage include lack of vaccines in low- and middle-income countries, such as Brazil. In high-income countries, such as the United Kingdom and Canada, remind-recall systems are particularly important. Challenges in the United States include the use of alternative vaccination calendars. This study highlighted important strategies adopted by PHC and the need to meet challenges, especially in middle- and low-income countries, in order to broaden access and improve vaccination coverage.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".