The Deadly Trio of Malaria, Pneumonia and Diarrhea: Assessing Community Knowledge Gaps and Beliefs within Integrated Community Case Management (iCCM) Practice in a Nigerian State
Bibliographic record
Abstract
The trio of Integrated Community Case Management (iCCM) target conditions, malaria, pneumonia and diarrhea, constitute a devastating global disease burden killing over 1 million under-5 children annually. Nigeria accounts for almost 15% of global under-5 mortality. The World Health Organization is promoting iCCM in resource-constrained societies. Therefore, this study assesses the knowledge of community members on iCCM target conditions. Health belief model serves as a theoretical guide for the study, which was conducted in three purposively selected Local Government Areas in Sokoto State, Nigeria. The study found that there are still some significant misconceptions despite the high prevalence of these diseases. About one-quarter of the respondents did not believe any of the diseases could kill a child. Socio-demographic variables have a significant effect on the knowledge gaps constituting barriers to the uptake of iCCM services. Despite the high usage of insecticide-treated bed-net, 68.9% of the respondents disclosed perceived malaria within the last 30 days preceding the survey. In all, 85.4% of the respondents have some religious barriers to attending health facilities. The study concluded that the diseases’ misconceptions limit the uptake of iCCM services where available. Therefore, knowledge of community members on iCCM needs to be improved.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".