Mapping of Pro-Equity Interventions Proposed by Immunisation Programs in Gavi Health Systems Strengthening Grants
Bibliographic record
Abstract
Reaching zero-dose (ZD) children, operationally defined as children who have not received a first dose of the diphtheria, tetanus, and pertussis (DTP1) vaccine, is crucial to increase equitable immunisation coverage and access to primary health care. However, little is known about the approaches already taken by countries to improve immunisation equity. We reviewed all Health System Strengthening (HSS) proposals submitted by Gavi-supported countries from 2014 to 2021 inclusively and extracted information on interventions favouring equity. Pro-equity interventions were mapped to an analytical framework representing Gavi 5.0 programmatic guidance on reaching ZD children and missed communities. Data from keyword searches and manual screening were extracted into an Excel database. Open format responses were analysed using inductive and deductive thematic coding. Data analysis was conducted using Excel and R. Of the 56 proposals included, 51 (91%) included at least one pro-equity intervention. The most common interventions were conducting outreach sessions, tailoring the location of service delivery, and partnerships. Many proposals had "bundles" of interventions, most often involving outreach, microplanning and community-level education activities. Nearly half prioritised remote-rural areas and only 30% addressed gender-related barriers to immunisation. The findings can help identify specific interventions on which to focus future evidence syntheses, case studies and implementation research and inform discussions on what may or may not need to change to better reach ZD children and missed communities moving forward.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".