Effectiveness of a health education intervention on the use of long-lasting insecticidal nets for the prevention of malaria in pregnant women of Pakistan: a quasi-experimental study
Bibliographic record
Abstract
Abstract Background About one quarter of pregnant women in the population of Pakistan are using long-lasting insecticide-treated bed nets (LLINs) for prevention of malaria. Past research reported that adequate information and education would act as mediator to change behaviour among patients for prevention of malaria infection. The effective use of LLINs would contribute to reduction of disease burden caused by malaria. The aim of this study was to determine the effectiveness of health education on the adoption of LLINs among pregnant women living in Tharparkar, a remote district in Sindh Province, Pakistan. Methods A quasi-experimental study design with control and intervention groups was conducted with 200 pregnant women (100 in each group). Women in the intervention group were provided with health education sessions on malaria for 12 weeks, while those in the control group obtained routine information from lady health workers (LHWs). Pre- and post-intervention assessment was done of knowledge about malaria and use of LLIN, which was statistically analysed using descriptive statistics and difference in difference (DID) multivariable regression analysis to test effectiveness of the intervention. Results Baseline was conducted with 200 pregnant women. Demographic characteristics were similar in both groups with slight differences in age, education, income, type of latrine, and source of drinking water. There were no significant differences between mean knowledge and use of LLINs scores between groups at baseline. However, the estimated DID value after the intervention was 4.170 (p < 0.01) and represents an increase in scores of knowledge in the intervention group compared to control. Similarly DID value of 3.360 (p < 0.05) showed an increase in use of LLINs score after the intervention which was significant, showing that the intervention had a positive effect. Conclusions Results proved that health education could be an effective intervention for improving knowledge and usage of LLINs among pregnant women for the prevention of malaria. Such educational interventions have a positive potential to be implemented at larger scale by incorporating them into routine health sessions provided by health workers.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".