Assessing knowledge about lymphatic filariasis and the implementation of mass drug administration amongst drug deliverers in three districts/cities of Indonesia
Bibliographic record
Abstract
BACKGROUND: This research assesses knowledge amongst drug deliverers about the implementation of mass drug administration (MDA) for lymphatic filariasis (LF) in Agam District (West Sumatera Province), the City of Depok (West Java Province) and the City of Batam (Kepulauan Riau Province), Indonesia. METHODS: A cross-sectional survey was conducted from January to March 2015 at these three sites. Respondents were identified using purposive sampling (i.e. cadre, health worker or community representatives). A total of 318 questionnaires were accepted for analysis. Three outcomes were assessed: knowledge about LF; knowledge about MDA implementation; and was informed about MDA coverage. Logistic regression analyses were employed to examine factors associated with these three outcomes. RESULTS: Less than half of respondents were charactersised as having a high level of LF knowledge and less than half a high level of knowledge about MDA. The odds of having a high level of knowledge of LF was significantly lower in Batam City than Agam District, yet higher amongst health workers than cadres. Deliverers living in urban areas reported more feedback on MDA outcomes than in the rural district. Health workers received more feedback than cadres (P < 0.001). Deliverers perceived the difference between coverage (drug receipt) and compliance (drug ingestion) in the community. CONCLUSIONS: There are variations in knowledge about LF and MDA as well as feedback across drug deliverers in MDA across geographical areas. Adaptation of the MDA guidelines, supportive supervision, increasing the availability of supporting materials and directly-observed therapy might be beneficial to improve coverage and compliance in all areas.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".