A user-centered approach to developing a new tool measuring the behavioural and social drivers of vaccination
Bibliographic record
Abstract
BACKGROUND: Children around the world remain under-vaccinated for many reasons. To develop effective vaccine delivery programmes and monitor intervention impact, vaccine programme implementers need to understand reasons for under-vaccination within their local context. The World Health Organization (WHO) Working Group on the Behavioural and Social Drivers of Vaccination (BeSD) is developing standardised tools for assessing childhood vaccine acceptance and uptake that can be used across regions and countries. The tools will include: (1) a validated survey; (2) qualitative interview guides; and (3) corresponding user guidance. We report a user-centred needs assessment of key end-users of the BeSD tools. METHODS: Twenty qualitative interviews (Apr-Aug 2019) with purposively sampled vaccine programme managers, partners and stakeholders from UNICEF and WHO country and regional offices. The interviews assessed current systems, practices and challenges in data utilisation and reflections on how the BeSD tools might be optimised. Framework analysis was used to code the interviews. RESULTS: Regarding current practices, participants described a variety of settings, data systems, and frequencies of vaccination attitude measurement. They reported that the majority of data used is quantitative, and there is appetite for increased use of qualitative data. Capacity for conducting studies on social/behavioural drivers of vaccination was high in some jurisdictions and needed in others. Issues include barriers to collecting such data and variability in sources. Reflecting on the tools, participants described the need to explore the attitudes and practices of healthcare workers in addition to parents and caregivers. Participants were supportive of the proposed mixed-methods structure of the tools and training in their usage, and highlighted the need for balance between tool standardisation and flexibility to adapt locally. CONCLUSIONS: A user-centred approach in developing the BeSD tools has given valuable direction to their design, bringing the use of behavioural and social data to the heart of programme planning.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".