A scoping review of interventions for vaccine stock management in primary health-care facilities
Bibliographic record
Abstract
One of the challenges facing the success of immunization programs is shortages of vaccines at health facilities, which could result from inadequate vaccine stock management. Several approaches have been designed by countries to improve vaccine stock management. This review summarizes currently available information on interventions for vaccine stock management.We considered both randomized trials and non-randomized studies eligible for inclusion in this review. The following databases were searched: PubMed, Embase, Cochrane Central Register of Controlled Trials, World Health Organization Library Information System, Web of Science, and PDQ-Evidence. We searched the websites of the World Health Organization, Global Alliance for Vaccine and Immunization, PATH's Vaccine Resources Library, and United Nations Children's Fund. The reference lists of all the included studies were also searched. Two authors independently screened search outputs, reviewed full texts of potentially eligible articles, evaluated risk of bias, and extracted data; resolving disagreements through consensus.Four studies met our inclusion criteria (three before-after studies and one randomized trial). Three studies were conducted in low- and middle-income countries while one was conducted in Canada (a high-income country). All the studies had various limitations and were classified as having a high risk of bias. Study findings suggest that the use of digital information systems to improve information and stock visibility, coupled with other interventions (such as training of health-care workers on the use of innovative tools and redesign of the supply chain to tackle certain bottlenecks), has the potential to increase vaccine availability, reduce response times, and improve the quality of vaccine records.
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 | 0.021 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".