Global compliance with hepatitis b vaccine birth dose and factors related to timely schedule. A literature review
Bibliographic record
Abstract
Objectives: Identify global barriers for delivery of hepatitis B vaccine birth dose. Methods: A search for cross sectional studies published between January 2001 and December 2017 was conducted using the following Mesh terms: "Vaccination"[Mesh], "Mass Vaccination"[Mesh], "Hepatitis B"[Mesh], "Hepatitis B virus"[Mesh], "Hepatitis B Surface Antigens"[Mesh]. Databases consulted included: PUBMED, SCIELO, EMBASE and BIREME. To evaluate the quality of studies, we used an adapted version of the Newcastle-Ottawa Quality Assessment Scale for cross sectional studies. Results: An initial list of 6,789 articles were generated by the combination of search terms. After reviewing titles and abstracts, they were reduced to 134 for full reading, and 22 studies were included in the barriers analysis. The region with more references was Western Pacific while eastern Mediterranean had the lowest. Being born outside of a health facility and weakness of outreach vaccination service seems to be the most important an cited factors related to underperformance of birth dose delivery. In developed countries, hospital policies on birth dose vaccination was the main factor associated to no vaccintion with the birth dose. Conclusions: New ways to deliver hepatitis B vaccines to neonates being born at home or outside health facilities should be envisaged and applied, if the goal of eliminating perinatal transmission of hepatitis B is to be achieved.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".