Assessing healthcare providers' knowledge and practices relating to insecticide-treated nets and the prevention of malaria in Ghana, Laos, Senegal and Tanzania
Bibliographic record
Abstract
BACKGROUND: Research evidence is not always being disseminated to healthcare providers who need it to inform their clinical practice. This can result in the provision of ineffective services and an inefficient use of resources, the implications of which might be felt particularly acutely in low- and middle-income countries. Malaria prevention is a particularly compelling domain to study evidence/practice gaps given the proven efficacy, cost-effectiveness and disappointing utilization of insecticide-treated nets (ITNs). METHODS: This study compares what is known about ITNs to the related knowledge and practices of healthcare providers in four low- and middle-income countries. A new questionnaire was developed, pilot tested, translated and administered to 497 healthcare providers in Ghana (140), Laos (136), Senegal (100) and Tanzania (121). Ten questions tested participants' knowledge and clinical practice related to malaria prevention. Additional questions addressed their individual characteristics, working context and research-related activities. Ordinal logistic regressions with knowledge and practices as the dependent variable were conducted in addition to descriptive statistics. RESULTS: The survey achieved a 75% response rate (372/497) across Ghana (107/140), Laos (136/136), Senegal (51/100) and Tanzania (78/121). Few participating healthcare providers correctly answered all five knowledge questions about ITNs (13%) or self-reported performing all five clinical practices according to established evidence (2%). Statistically significant factors associated with higher knowledge within each country included: 1) training in acquiring systematic reviews through the Cochrane Library (OR 2.48, 95% CI 1.30-4.73); and 2) ability to read and write English well or very well (OR 1.69, 95% CI 1.05-2.70). Statistically significant factors associated with better clinical practices within each country include: 1) reading scientific journals from their own country (OR 1.67, 95% CI 1.10-2.54); 2) working with researchers to improve their clinical practice or quality of working life (OR 1.44, 95% CI 1.04-1.98); 3) training on malaria prevention since their last degree (OR 1.68, 95% CI 1.17-2.39); and 4) easy access to the internet (OR 1.52, 95% CI 1.08-2.14). CONCLUSIONS: Improving healthcare providers' knowledge and practices is an untapped opportunity for expanding ITN utilization and preventing malaria. This study points to several strategies that may help bridge the gap between what is known from research evidence and the knowledge and practices of healthcare providers. Training on acquiring systematic reviews and facilitating internet access may be particularly helpful.
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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 | 0.003 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".