Mind the gap: scaling up the utilization of insecticide treated mosquito nets using a knowledge translation model in Isingiro district, rural south western Uganda
Bibliographic record
Abstract
Background: The phenomenon of Knowledge Translation (KT) is a key intervention towards bridging the ‘know–do’ gap. We conducted a KT initiative in Isingiro district to positively change attitude and improve on the uptake of Insecticide Treated Mosquito Nets (ITNs) as a malaria prevention strategy.Methods: This was a community based interactive initiative that was carried out within the seventeen administrative units of Isingiro district using varied dissemination activities, namely: health talks; drama activities, and the sharing of ITNs success stories.Results: We reached out to 34 dissemination groups, comprising communal gathering, religious crusades, open markets, secondary schools, and district administration. In addition, we spot-visited 46 households to ascertain the physical presence of ITNs, and their appropriate use. The major intervention was improved knowledge base of malaria causation and prevention strategies. The indicators for improved knowledge were hinged on the five-interventions, namely: (a) communal sensitization on malaria to provide, (b) monitoring and support of selected households, (c) emphasis of ITN use as a malaria prevention strategy, (d) promotion of care for ITNs, and (e) promotion of ITN use. In all, the major output was improved knowledge base of malaria causation and prevention strategies by providing accurate information to redress the myths and misconceptions related to malaria and ITNs use.Conclusion: This undertaking describes a consolidated community intervention to promote ITN utilization. It is plausible that this intervention positively enhances and promotes uptake and utilization of ITNs.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".