Understanding the factors that shape vaccination ecosystem resilience: a qualitative assessment of international expert experiences and perspectives
Bibliographic record
Abstract
Introduction: 'Shocks' or external stressors to vaccination programmes can lead to decreased vaccination coverage rates. The capacity of vaccination ecosystems to effectively respond and adapt to shocks demonstrates programme resilience. This study sought to describe components that contribute to resilience in national immunisation programmes. Methods: Mixed-methods study comprising in-depth interviews and surveys with n=30 vaccination programme experts in eight countries (Brazil, Costa Rica, Greece, Japan, Nigeria, Philippines, Spain and the USA). We elicited data on country-specific shocks, associated effects and factors that facilitated or impeded programme resilience. Interviews and open-ended survey responses were analysed qualitatively, with closed-ended survey questions analysed using descriptive statistics. Results: Experts described immediate effects of shocks including decreased vaccine uptake and negative perceptions of vaccination from the public and media. Late emerging impacts included increased vaccine hesitancy and vaccine-preventable disease (VPD) rates. Stakeholder education, immunisation information systems (IIS) and programme financing were key factors to strengthening programme resilience. Appropriately trained frontline healthcare personnel can counter vaccine misinformation that otherwise erodes trust and contributes to hesitancy. The COVID-19 pandemic also exposed structural weaknesses in programme resilience, with experts highlighting the need for robust IIS and workforce support to mitigate burnout and strengthen resilience when a shock occurs. Conclusions: Our findings provide preliminary insights into factors that experts believe to be associated with vaccination programme resilience. Anticipating, adapting and responding to shocks is central to strengthening systems, ensuring ecosystem resilience and protecting against current and future VPD threats.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".