A mixed-methods exploration into the resilience of community drug distributors conducting mass drug administration for preventive chemotherapy of lymphatic filariasis and onchocerciasis in Côte d’Ivoire and Uganda
Bibliographic record
Abstract
Volunteer community drug distributors (CDDs) have been vital to progress made in the elimination of onchocerciasis and lymphatic filariasis; two neglected tropical diseases amenable to preventive chemotherapy (PC-NTDs). However, formative work in Côte d'Ivoire and Uganda revealed that CDDs can encounter considerable challenges during mass drug administration (MDA). CDDs must be resilient to overcome these challenges, yet little is known about their resilience. This mixed-methods study explored the resilience of CDDs in Côte d'Ivoire and Uganda. The characteristics and experiences of 248 CDDs involved in the 2018 MDAs in Côte d'Ivoire (N = 132) and Uganda (N = 116) were assessed using a micronarrative survey. Thematic analysis of CDDs' micronarratives was used to identify challenges they encountered during MDA. Resilience was assessed using the Connor-Davidson Resilience Scale 25 (CD-RISC-25). Variables from the micronarrative survey found to be individually associated with mean CD-RISC-25 score (P<0.05) through bivariate analyses were included in a multiple linear regression model. Post-hoc, country-specific analyses were then conducted. Thematic analysis showed that CDDs encountered a wide range of challenges during MDA. The aggregate model revealed that CDDs who had positive relationships or received support from their communities scored higher on the CD-RISC-25 on average (P<0.001 for both), indicating higher resilience. These trends were also observed in the country-specific analyses. Mean CD-RISC-25 scores were unaffected by variations in district, age, gender, and length of involvement with the NTD program. Community support during MDA and positive community-CDD relationships appear to be associated with CDDs' personal capacity to overcome adversity. Involving communities and community leadership in the selection and support of CDDs has the potential to benefit their well-being. This study establishes the CD-RISC-25 as a useful tool for assessing the resilience of CDDs. Further research is needed to understand, promote, and support the resilience of this valuable health workforce, upon which NTD programs depend.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".