“Wherever doctors cannot reach, the sunshine can”: overcoming potential barriers to malaria elimination interventions in Haiti
Bibliographic record
Abstract
BACKGROUND: Haiti and the Dominican Republic, the only two Caribbean countries with endemic malaria transmission, are committed to eliminating malaria. With a Plasmodium falciparum prevalence under 1% and a highly focal transmission, the efforts towards elimination in Haiti will include several community-based interventions that must be tailored to the local sociocultural context to increase their uptake. However, little is known about local community perceptions regarding malaria and the planned elimination interventions. The aim of this study is to develop a robust understanding of how to tailor, implement and promote malaria elimination strategies in Haiti. METHODS: A cross-sectional qualitative study was conducted December 2015-August 2016 in Grande-Anse and the North Department in Haiti. Data collection included key informant interviews (n = 51), in-depth interviews (n = 15) and focus group discussions (n = 14) with health workers, traditional healers, teachers, priests or pastors, informal community leaders, public officials, and community members. Following a grounded theory approach, transcripts were coded and analysed using content analysis. Coded text was sorted by the types of interventions under consideration by the malaria elimination programme. RESULTS: The level of knowledge about malaria was low. Many participants noted community beliefs about malaria being caused by magical phenomena in addition to vector-borne transmission. Participants described malaria as a problem rooted in the environment, with vector control the most noted method of prevention. Though participants noted malaria a severe disease, it ranked lower than other health problems perceived as more acute. Access barriers to healthcare were described including a lack of bed nets. Some distrust about pills, tests, and foreigners in general was expressed, and in few cases linked to previous experience with malaria campaigns under dictatorial regimes. CONCLUSIONS: There are several potential barriers and opportunities to implement community-based malaria elimination interventions in rural Haiti. Elimination efforts should include the collaboration of voodoo priests and other traditional healers, be coupled with solutions to wider community concerns or other health interventions, and learn from previous or similar programmes, such as the campaign to eliminate lymphatic filariasis. It is essential to engage with communities and gain their trust to successfully implement targeted aggressive elimination activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".