Incorporating qualitative research methods into the monitoring and evaluation of neglected tropical disease programmes: a scoping literature review
Bibliographic record
Abstract
This publication addresses the limited use of qualitative methods in neglected tropical disease (NTD) programmes. It describes a scoping literature review conducted to inform the development of a guide to inform the use of rapid qualitative assessments to strengthen NTD mass drug administration (MDA) programmes. The review assessed how qualitative methods are currently used by NTD programmes and identified qualitative approaches from other health and development programmes with the potential to strengthen the design of MDA interventions. Systematic review articles were reviewed and searched using key terms conducted on Google Scholar and PubMed. Results show that methods used by NTD programmes rely heavily on focus group discussions and in-depth interviews, often with time-consuming analysis and limited information on how results are applied. Results from other fields offered insight into a wider range of methods, including participatory approaches, and on how to increase programmatic uptake of findings. Recommendations on how to apply these findings to NTD control are made. The topic of human resources for qualitative investigations is explored and a guide to improve MDAs using qualitative methods is introduced. This guide has direct applicability across the spectrum of NTDs as well as other public health programmes.
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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 | 0.282 | 0.367 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.028 | 0.029 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| 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".