Factors Associated with Levels of Latrine Completion and Consequent Latrine Use in Northern Ghana
Bibliographic record
Abstract
Open defecation is still a major health problem in developing countries. While enormous empirical research exists on latrine coverage, little is known about households' latrine construction and usage behaviours. Using field observation and survey data collected from 1523 households in 132 communities in northern Ghana after 16 months of implementation of Community Led Total Sanitation (CLTS), this paper assessed the factors associated with latrine completion and latrine use. The survey tool was structured to conform to the Risk, Attitude, Norms, Ability and Self-regulation (RANAS) model. In the analysis, we classified households into three based on their latrine completion level, and conducted descriptive statistics for statistical correlation in level of latrine construction and latrine use behaviour. The findings suggest that open defecation among households reduces as latrine construction approaches completion. Although the study did not find socio-demographic differences of household to be significantly associated with level of latrine completion, we found that social context is a significant determinant of households' latrine completion decisions. The study therefore emphasises the need for continuous sensitisation and social marketing to ensure latrine completion by households at lower levels of construction, and the sustained use of latrines by households.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".