Immunization Coverage of Children in Care of the Child Welfare System in High-Income Countries: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Children in care of the child welfare system tend to underutilize preventive health services compared with other children. The purpose of this systematic review was to assess current knowledge regarding immunization coverage levels for children in the child welfare system and to determine barriers and supports to them utilizing immunization services. EVIDENCE ACQUISITION: Articles published in Medline, Embase, Cochrane Library, CINAHL, SocINDEX, and ERIC from January 1, 2000 to October 13, 2017 were searched. Thesis and conference databases and relevant websites were also examined. Studies were included if written in English, from high-income countries, and addressed immunizations for children in the child welfare system. Independent dual screening, extraction, and quality appraisal were conducted between October 2016 and December 2017, followed by narrative synthesis. EVIDENCE SYNTHESIS: Of 2,906 records identified, 33 met inclusion criteria: 21 studied coverage, two studied barriers/supports, and ten studied both. Nineteen studies were moderate or high quality and thus included in the narrative synthesis; 15 studied coverage, one studied barriers/supports, and three studied both. Most studies found lower coverage among children in child welfare. The few studies that explicitly studied barriers/supports to immunization identified that a collaborative and coordinated approach between health and social services was key to service delivery to this population. CONCLUSIONS: This review highlights that children in care of the child welfare system are at risk of poor immunization coverage. There is a need for high-quality studies on this issue, with a focus on assessing supports/barriers to immunization in this population.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".