Felix Adipare. A tailored approach: Multiple innovations in service delivery to improve coverage in Ghana (IA2030 Case study 24)
Bibliographic record
Abstract
Felix recounts how he has drawn inspiration from the Movement for Immunization Agenda 2030 (IA2030) in his efforts to improve vaccination coverage in his district. These learnings helped boost vaccine coverage from 60% to 99% in just a year. In March 2022, he joined the Movement for Immunisation Agenda 2030 (IA2030) – a collective of more than 16,000 health workers committed to achieving the goals set out in IA2030, the world’s immunisation strategy. Members share ideas and experiences across borders to help each other with the challenges they face. The Movement for IA2030 is facilitated by the Geneva Learning Foundation (TGLF), a Swiss non-profit organisation that implements large-scale, locally led peer learning programmes for health. According to Felix, participation in the Movement for IA2030 was crucial to this success. The aftershock of the COVID-19 pandemic placed enormous stress on him and his team. Connecting with peers in a similar situation helped him realise he was not alone and provided a vital peer support network. And participation in the Movement’s peer learning activities exposed him to many new ideas he could adopt locally – the use of private facilities, for example, was a strategy he learned from the “IA2030 Ideas Engine”, a repository of ideas and activities shared by Movement members. About this IA2030 case study This Immunization Agenda 2030 (IA2030) case study is part of a series shining a light on the experiences of immunization and primary healthcare staff working at different levels of national immunization programmes in low- and middle-income countries. The people featured are all taking part in the IA2030 Movement peer learning programme organized by the Geneva Learning Foundation (TGLF). Each case study forms part of the IA2030 Movement’s Knowledge-to-Action Hub, designed to promote the application of knowledge shared by Movement members. Learn more about the Hub… Learn more about the Movement… This report is part of series aiming to capture the perspectives of a diverse group of health practitioners working to deliver or manage immunization services in low- and middle-income countries. Contributing to consultative engagement between international and local levels, each report offers a unique opportunity to discover unfiltered experiences and insights from thousands of people whose daily lives revolve around delivering immunization services.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".