Effect of A Community Health Worker Led Mobile Phone Intervention in Surveillance and Control of Bedbugs in Nakuru County; A Study Protocol
Bibliographic record
Abstract
Globally there has been a resurgence of bed bug infestation after decades of suppression using modern pesticides such as pyrethroids. The dramatic rise in bed bug infestation has been reported in Canada, USA, Australia and Africa causing panic and significant public attention. Bed bug is widely found in temperate and in sub-tropical countries and is broadly distributed in regions north and south of the equator. Bed bug infestation is shallowly studied and thus limited information regarding the parasite especially in developing countries such as Kenya. For instance, in Ethiopia bed bug infestation is one of the most neglected and underreported and understudied Phenomenon due to high diseases burden. Based on a 2015 media report about 4000 homes in Nakuru county were found to be infested by bed bugs. In 2017, 7 estates in Nakuru county were reported to have been heavily infested with bed bugs with about 5000 households being affected. The general objective of this study is to establish effect of a Community Health Worker (CHW) led mobile phone intervention in surveillance and control of bedbugs in Nakuru County. The specific Objectives of this study are to; establish proportion of households infested with bedbugs in Nakuru county, determine socio demographic and socioeconomic factors associated with bed bug infestation among households in Nakuru county, establish the effect of a Community Health Worker (CHW) led mobile based application in detection and reporting (surveillance) of bed bag infestation among household in Nakuru county, and to establish effect of a Community Health Worker intervention in Control of bed bugs infestation among households in Nakuru County. The study deign will be a quasi-experiment conducted in intervention and control sites. Flamigo and Kivumbini wards will be the intervention and Menengai ward control sites. Sample size for intervention and control sites has been calculated as 354 and 362 households respectively. Purposive and systematic sampling methods will be used to identify the study participants. Proportions, measures of central tendency, Chi square tests and Odds ratios will be used tin data analysis. Difference in Differences, and regression analysis will be used to test study hypotheses. Appropriate tables and graphs will be used for data presentation. This study protocol was subjected to Mt. Kenya University Ethical approval board for review and approval. The study was licensed by the National Council of Science and Technology (NACOSTI) in Kenya.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".