Vaccine equity implementation: exploring factors influencing COVID-19 vaccine delivery in the Philippines from an equity lens
Bibliographic record
Abstract
BACKGROUND: During the early phase of the COVID-19 vaccine rollout, low and middle-income countries (LMICs) were facing challenges in achieving equitable vaccine delivery. Few studies have contextualized global vaccine distributive injustice into national-specific contexts to understand its impact on vaccine delivery from an equity perspective. We aimed to investigate factors influencing equitable COVID-19 vaccine delivery in the Philippines and to provide recommendations to enhance equitable vaccine delivery in LMICs to prepare for future health emergencies. METHODS: The Health Equity Implementation Framework was employed to guide this qualitative study. We recruited participants using purposeful and snowballing sampling strategies. Semi-structured interviews were conducted with participants in person, online, or over the phone. A reflective thematic analysis approach was employed to analyze data. RESULTS: We recruited 38 participants including seven high-level stakeholders from the public and private sectors, 14 health workers, and 17 community members in the province of Negros Occidental, Philippines. Equitable delivery of COVID-19 vaccines was influenced by an interplay of multiple factors operating in different domains. Contextually, the rapidly evolving nature of the COVID-19 virus, ongoing scientific advancements, and international negotiations directed national-level vaccine policies. Political commitment and support were recognized as crucial drivers for successful vaccine delivery, with a strong emphasis on health information framing and communication and adherence to human rights principles. The vulnerability of the health system significantly impacted the timely and effective distribution of vaccines. Furthermore, the geographical characteristics of the Philippines presented unique logistical challenges to vaccine delivery. At the recipient domain, individual perceptions of vaccines, shaped by their socioeconomic status, exposure to (mis)information, social influence, and entrenched religious beliefs, played a major role in their vaccine decisions and thus vaccine coverage regionally. Additionally, vaccine characteristics and operational challenges related to its distribution also impacted fair allocation. CONCLUSIONS: The findings highlight the urgent need for LMICs to strengthen their health system resilience and sustainability and use multilevel strategies to build public trust to improve vaccine uptake and coverage. Moreover, each LMIC must be attentive to its unique contextual factors to develop tailored implementation strategies to promote equitable vaccine distribution.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".