Interventions designed to improve vaccination uptake: Scoping review of systematic reviews and meta-analyses - protocol (version 1)
Bibliographic record
Abstract
Abstract Background Vaccine uptake varies substantially, and resources to promote the uptake of vaccines differ widely by country and income level. As a result, immunization rates are often suboptimal. There is a need to understand what works, particularly in low- and middle-income countries and other settings where resources are scarce. Methods We plan to conduct a scoping review of interventions designed to increase vaccination uptake We will include systematic reviews and meta-analyses of interventional studies that address the question of vaccine uptake. We will search the following electronic databases: MEDLINE, Cochrane Database of Systematic Reviews, EMBASE, Epistemonikos, Google Scholar, LILACs and TRIP database (which covers guidelines and the grey literature) until 01 July 2021 and hand-search the reference lists of included articles. We will include systematic reviews that comprise studies of all ages if they report quantitative data on the impact on vaccine uptake. To assess the quality, we will use a modified AMSTAR score and ate the quality of the evidence in included reviews using the “Grade of Recommendations Assessment, Development and Evaluation” (GRADE). Expected results We intend to present the evidence using summary tables to present the evidence stratified by vaccine coverage, the specific population, e.g., children, adolescents and older adults, and by setting, e.g. healthcare, community. We will also present when low middle-income subgroups are reported.
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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 | 0.076 | 0.147 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.018 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.086 | 0.011 |
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".