An integrated human behavioral model for mosquito-borne disease control: A scoping review of behavior change theories used to identify key behavioral determinants
Bibliographic record
Abstract
Mosquito-borne disease (MBD) control depends largely on a range of public health measures aimed at reducing the spread of infected mosquitoes and human-mosquito contact. These public health measures are generally driven by voluntary, though in few occasions obligatory (e.g., indoor residual spraying), self-protective behaviors by individuals and communities. To develop effective interventions that promote public health measures, the underlying mechanisms that contribute to self-protective behaviors should be well understood. The present scoping review aims to provide a timely overview of how behavior change theories have been applied in the context of MBD control. In addition, the review proposes an integrated model that includes identified key determinants in MBD control behavior, and identifies knowledge gaps to inform future research. A comprehensive search was performed in several databases: MEDLINE, PsycINFO, Embase (Ovid), Web of Science Core Collection, CINAHL, ERIC, and Econ.Lit (EBSCO), as well as registered trials and reviews in CENTRAL and PROSPERO to identify ongoing or unpublished studies. References of included studies and literature reviews were screened, as well as citation tracking in Web of Science, Google Scholar and the malaria database of Behavior Change Impact. This scoping review identified a total of 28 studies. Most studies targeted personal-protective behavioral measures such as adopting, using, or maintaining insecticide-treated bed nets, and were most frequently informed by risk-related behavioral theories. Knowledge and perceived susceptibility of the risk, and related perceived efficacy were identified as key behavioral determinants in the conceptual, integrated human behavior model for MBD control. Numerous studies related to MBD control behavior, especially those focusing on knowledge-attitudes-practices (KAP), often lack a solid theoretical framework, which risks depicting an incomplete understanding of behaviors. In addition, by incorporating various behavioral disciplines into the domain of MBD control, a more comprehensive understanding of key behavioral determinants may be developed and applied in future research and MBD control efforts.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".