Vaccination barriers and drivers in Romania: a focused ethnographic study
Bibliographic record
Abstract
BACKGROUND: In 2016-18, a large measles outbreak occurred in Romania identified by pockets of sub-optimally vaccinated population groups in the country. The aim of the current study was to gain insight into barriers and drivers from the experience of measles vaccination from the perspectives of caregivers and their providers. METHODS: Data were collected by non-participant observation of vaccination consultations and individual interviews with health workers and caregivers in eight Romanian clinics with high or low measles vaccination uptake. Romanian stakeholders were involved in all steps of the study. The findings of this study were discussed during a workshop with key stakeholders. RESULTS: Over 400 h of observation and 161 interviews were conducted. A clear difference was found between clinics with high and low measles vaccination uptake which indicates that being aware of and following recommended practices for both vaccination service delivery and conveying vaccine recommendations to caregivers may have an impact on vaccine uptake. Barriers identified were related to shortcomings in following recommended practices for vaccination consultations by health workers (e.g. correctly assessing contraindications or providing enough information to allow an informed decision). These observations were largely confirmed in interviews with caregivers and revealed significant knowledge gaps. CONCLUSIONS: The identification of key barriers provided an opportunity to design specific interventions to improve vaccination service delivery (e.g. mobile vaccination clinics, use of an electronic vaccination registry system for scheduling of appointments) and build capacity among health workers (e.g. guidance and supporting materials and training programmes).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".