Methodologies to measure the coverage of vitamin A supplementation: a systematic review
Bibliographic record
Abstract
Countries are increasingly transitioning from event-based vitamin A supplementation (VAS) distribution to delivery through routine health system contacts, shifting also to administrative, electronic-based monitoring tools, a process that brings certain limitations affecting the quality of administrative VAS coverage. At present, there is no standardised methodology for measuring the coverage of VAS delivered through routine health services. To address this gap, we conducted a systematic review of the literature to identify and recommend methods to measure VAS coverage, with the aim of providing guidance to countries on the collection of consistent data for planning, monitoring and evaluating VAS programmes integrated into routine health systems. We searched the PubMed®, Embase®, Scopus, Google Scholar and World Health Organization (WHO) Global Index Medicus databases for studies published from 1 January 2000 to 1 January 2021, reporting original data on VAS coverage and methodologies used for measurement. We screened 2371 original titles and abstracts, assessed twenty-seven full-text articles and ultimately included eighteen studies. All but two studies used a coverage cluster survey (CCS) design to measure VAS coverage, adapting the WHO Vaccination Coverage Cluster Surveys methodology, by modifying sample size and sampling parameters. Annual two-dose VAS coverage was reported from only four studies. Until electronic-based systems to collect and analyse VAS data are equipped to measure routine two-dose VAS coverage using administrative data, CCSs that comply with the 2018 WHO Vaccination Coverage Cluster Surveys Reference Manual represent the gold-standard method for effective VAS programme monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.138 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".