Proxy reporting in the National Population Health Survey.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: This article examines the extent of proxy reporting in the National Population Health Survey (NPHS). It also explores associations between proxy reporting status and the prevalence of selected health problems, and investigates the relationship between changes in proxy reporting status and two-year incidence of health problems. DATA SOURCE: Cross-sectional results are based on the 1996/97 NPHS Health file and General file. Longitudinal results are based on 1994/95 respondents who were still residing in households in 1996/97. ANALYTICAL TECHNIQUES: The extent of proxy reporting in the various NPHS files was computed. Prevalence estimates of selected health problems from the two 1996/97 cross-sectional files were compared. Multivariate analyses were used to estimate associations between proxy reporting status and health problems. MAIN RESULTS: For several health conditions, prevalence estimates based on the 1996/97 cross-sectional Health file (where proxy reporting was less common) were significantly higher than estimates derived from the General file. Individuals whose data were proxy-reported in 1994/95 and self-reported in 1996/97 had higher odds of reporting new cases of certain health conditions.
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.
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 | 0.208 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it