Experience With Digital Entry Of National Iodine Survey Data In Senegal
Bibliographic record
Abstract
There is growing evidence and enthusiasm for the use of technology to enhance accuracy and speed and minimize the costs of quantitative data surveys. Handheld computers have the potential to facilitate high quality data collection and timely analysis for large complex surveys in developing country contexts. The objective of this paper is to document the experience of using handheld computers for direct data capture in a national survey to monitor progress toward universal salt iodization in Senegal. Twenty-five personal digital assistants (PDAs) were programmed and used by five teams across 13 regions for entering data from 3768 households over a period of three weeks. The health staff selected to collect the data learned how to manipulate the PDAs within a short amount of time. The PDAs contributed to improved quality of data collection due to automatic skipping of non-applicable questions and selection of individuals eligible for goitre assessment. The PDAs also were programmed to randomly select one woman and one school-age child within each household for biochemical sample collection. Data on geographic locations were collected for 82% of households surveyed using the PDA’s in-built Global Positioning System (GPS) functionality, which showed the geographical dispersion of these households and which will be used for analysis of results for key indicators by location. Problems with household selection processes, identification coding and standardized interview methods were observed. While costs for printing questionnaires and manual data entry were saved, significant costs were incurred for PDA technical support by an international consultant. The use of PDAs significantly reduced data processing time. Data were stored and downloaded to a central database, with the full dataset and preliminary results available to stakeholders within one week following the survey completion. The PDAs are an excellent tool for enhancing capacity to collect complex household survey data and make it available for analysis in a timely manner. Quality training and adequate pre-testing of questionnaires are still essential. Increased use of direct data capture methods in health program design, monitoring and evaluation is recommended, along with efforts to build local technical capacity.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".