Implementing automated subject selection methodology in an epidemiological survey using open source application, Open Data Kit (ODK) (Preprint)
Bibliographic record
Abstract
BACKGROUND Data collection in field-based public health research influences the quality and outcome of the research which in turn aids in developing evidence-based policymaking. As conventional data collection methods are either consume more time or more resources, usage of the cost-effective technology solution is required. Open Data Kit (ODK), the open-source application being designed for an Android-based platform is one such solution to electronic data collection. OBJECTIVE To demonstrate the functionality of ODK in implementing subject selection algorithm Kish methodology in epidemiological research studies METHODS Subject selection algorithms in field-based surveys are done manually and one such algorithm is Kish algorithm. Incorporating subject selection algorithm electronically in ODK application was not either tested or reported in the literature. We developed an application framework to build-in Kish grid algorithm in ODK application RESULTS The process and steps for setting up subject selection Kish algorithm to embed in ODK application are explained with syntax function. The application displayed the selected eligible adult on the screen after it runs the syntax function based on the Kish algorithm in the backend CONCLUSIONS ODK is an enterprise architecture, which is identified to develop a mobile application for a community based public health survey. The application would reduce the time taken for selecting and completing the interview of study participants. Ever a minor change like spell check on form mandates complete XML generation in ODK application and requires to update the existing entire application
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.026 |
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".