Infant vaccination against malaria in Mozambique and in Togo: mapping parents’ willingness to get their children vaccinated
Bibliographic record
Abstract
Background: Malaria is one of the most widespread and deadly diseases worldwide and large majority of malaria cases and deaths occurs in Africa. Efforts to develop an effective vaccine against malaria are underway and several vaccine prototypes are on different clinical trial phases.Objective: As many sub-Saharan African countries have shown interest in introducing large-scale infant vaccination against malaria when a definitively approved vaccine will be available, the present study aimed at mapping Mozambican parents’ willingness to get their children vaccinated and comparing the results with findings from a similar study we conducted in Togo (209 participants).Methods: In Mozambique, 227 parents indicated their willingness to get their children vaccinated (using an 11-point scale) against malaria under different conditions varying as a function of the main constructs of health-protective theories: perceived risk of getting malaria, perceived severity of malaria, effectiveness of the vaccine, cost of the vaccine, and neighbors’ attitude toward vaccination. The participant responses were subjected to cluster analysis, ANOVA and Ch2 test.Results: Six qualitatively different positions were found, which were labeled Cost (12%); Neighbors, Risk, and Cost (28%); Treatment, Risk, and Cost (10%); Always Vaccinate (7%); Risk and Cost (13%); and Risk, Treatment, Effectiveness, and Cost (22%). These positions were associated with participants’ socio-demographic characteristics.Conclusion: A similar variety of parental positions on malaria vaccination was found in Mozambique and in Togo, which suggests that malaria vaccination campaigns in sub-Saharan African countries must be tailored in design and implementation to match the diversity of parents’ needs and views.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".