Does self-reported water collection time differ from observed water collection time? Evidence from the Upper West Region of Ghana
Bibliographic record
Abstract
Abstract Water collection time is a key indicator in tracking access to drinking water. Over the years, water surveillance agencies have relied on water collectors for information on water collection time to measure progress of water supply. However, emerging evidence shows that water collection times reported by water collectors in developing countries are often imprecise. To contribute to knowledge about the validity or otherwise of self-reported water collection times, this study explored the association between self-reported water collection times and observed water collection times in the Upper West Region of Ghana. Data on water collection times were gathered from 412 water collectors in Daffiama-Issa-Bussie District and Lawra Municipality, first through interviews, and second by observation. From the results, self-reported water collection times were significantly (P < 0.01) lower than observed water collection times. The average round-trip water collection time reported by water collectors (32 minutes) was 8 minutes lower than observed water collection times (40 minutes). This implies that existing statistics on basic water coverage that are largely based on self-reported water collection times are overestimated. Going forward, we call on water surveillance agencies to collect data on water collection times through observation. This article has been made Open Access thanks to the generous support of a global network of libraries as part of the Knowledge Unlatched Select initiative.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".