The feasibility of using remote data collection tools in field surveys
Bibliographic record
Abstract
Background: The objectives of the study were to conduct a field survey to measure the prevalence of chronic diseases by taking history, to assess the feasibility of using remote data collection tools in field surveys and to create the map of the survey area using global positioning system (GPS). Methods: A community survey was carried out in two urban municipal wards by trainees with medical sociology back ground among those aged 35 years and above. There were a total of 563 participants from whom history of chronic diseases were collected and from those aged 60 years and above the presence of frailty was assessed using Canadian Study of Health and Ageing (CSHA) Clinical Frailty Scale. The data was collected using a remote data collection application named KoBo Toolbox, downloaded in their smart phones, which was sent directly to the main computer in the Clinical Epidemiological Unit, using mobile data or Wi-Fi hotspots. The co-ordinates of the households were marked using GPS which was also sent through the KoBo Toolbox to the main computer. At the centre the data was converted into excel sheets and various percentages were calculated. Results: In the survey the proportion affected with diabetes, hypertension, coronary artery disease and cerebrovascular accidents were 24%, 20.6%, 10.5% and 3.5% respectively. Among the older population 2.2% were found to be severely frail or worse requiring special care. The field map of the area surveyed was also generated using the co-ordinates marked using the GPS enabled phones. Conclusions: The remote data collection tool enabled us to conduct a survey on chronic diseases, effectively, within a limited period of time, creating a map of the area surveyed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".