Nudge strategies for behavior-based prevention and control of neglected tropical diseases: A scoping review and ethical assessment
Bibliographic record
Abstract
BACKGROUND: Nudging, a strategy that uses subtle stimuli to direct people's behavior, has recently been included as an effective and low-cost behavior change strategy in low- and middle- income countries (LMIC), targeting behavior-based prevention and control of neglected tropical diseases (NTDs). The present scoping review aims to provide a timely overview of how nudge interventions have been applied within this field. In addition, the review proposes a framework for the ethical consideration of nudges for NTD prevention and control, or more broadly global health promotion. METHODS: A comprehensive search was performed in several databases: MEDLINE, PsycINFO, and 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ERIC and Econ.Lit (EBSCO), as well as registered trials and reviews in CENTRAL and PROSPERO to identify ongoing or unpublished studies. Additionally, studies were included through a handpicked search on websites of governmental nudge units and global health or development organizations. RESULTS: This scoping review identified 33 relevant studies, with only two studies targeting NTDs in particular, resulting in a total of 67 nudge strategies. Most nudges targeted handwashing behavior and were focused on general health practices rather than targeting a specific disease. The most common nudge strategies were those targeting decision assistance, such as facilitating commitment and reminder actions. The majority of nudges were of moderate to high ethical standards, with the highest standards being those that had the most immediate and significant health benefits, and those implemented by agents in a trust relationship with the target audience. CONCLUSION: Three key recommendations should inform research investigating nudge strategies in global health promotion in general. Firstly, future efforts should investigate the different opportunities that nudges present for targeting NTDs in particular, rather than relying solely on integrated health promotion approaches. Secondly, to apply robust study designs including rigorous process and impact evaluation which allow for a better understanding of 'what works' and 'how it works'. Finally, to consider the ethical implications of implementing nudge strategies, specifically in LMIC.
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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 | 0.259 | 0.499 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.026 | 0.019 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.010 | 0.006 |
| 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".