Strategies to promote COVID-19 vaccination in Northern Ghana: a qualitative study of stakeholders’ perspectives
Bibliographic record
Abstract
BACKGROUND: Despite having sufficient vaccines to inoculate 88% of eligible individuals with at least one dose, COVID-19 vaccine hesitancy in Ghana has been 42.0%. This study explored context-specific strategies to promote COVID-19 vaccination in northern Ghana. METHODS: A descriptive qualitative study design guided this project. The stakeholder group included directors of health services, public health, disease control, health promotion officers, and traditional medicine healers. A total of 17 stakeholders were interviewed. Interviews were scheduled and conducted using Zoom calls. Zoom invitations were sent out, and each interview lasted 30 to 45 min. Reflexive thematic analysis was employed to analyze the data using NVivo. RESULTS: Five themes emerged from the study: (1) the organization of vaccination programs, (2) organizational support for vaccination programs, (3) proposed strategies to enhance intake, (4) challenges encountered in vaccination programs, and (5) the exploration of herbal medicine as an alternative option. CONCLUSION: Our study provides vital insights into the strategies employed, challenges, and strategies that could help tackle COVID-19 vaccine hesitancy in northern Ghana. These findings suggest that area-specific evaluation should be conducted to understand the peculiar logistics needed to support future vaccination programmes, and prioritization of people living with disability.
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 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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, 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".