Is The Mobile Phone Technology Feasible For Effective Monitoring Of Defecation Practices In Ghana? The Case Of A Peri-Urban District In Ghana
Bibliographic record
Abstract
Background: The world leaders have decided to increase the sanitation coverage in areas of with poor access and monitor the progress. However, data collection via existing paper-based monitoring and evaluation (M & E) survey tools has limitations, including the approach used in Ghana. Therefore, there is the need to test new innovative M & E tools for monitoring sanitation practices. Objective: To compare a mobile phone short messaging service (SMS) M & E survey tool with a paper based format in a rapidly expanding peri-urban setting of Prampram, Ghana. Methods: Four hundred and fifty-eight adults with access to a mobile phone were purposely selected. Next, they were randomly assigned to the group using SMS or the group reporting on sanitation practices through a paper-based survey method. Respondents were asked to answer 5 questions on sanitation practices once every quarter over a one-year period. Non-responders were interviewed to ascertain reasons for non-response. A subset of 227 respondents were interviewed to obtain information on acceptability, ease of use and level of privacy of the two M & E survey tools. Results: Respondents from this study, found the mobile phone SMS M & E survey tool to be feasible although the tool was unacceptable, not user friendly and offered a low level of privacy as compared to the paper tool. Conclusions: The mobile phone SMS M & E tool cannot replace paper-based tool for sanitation M & E in Ghana. Further studies could examine alternative mobile phone applications, for example the use of pictorial mobile phone technology for data collection among lowliteracy populations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".