Validity of Self‐Report Data in Hypertension Research: Findings From The Study on Global Ageing and Adult Health
Bibliographic record
Abstract
Several studies indicate little congruence between self-report and biometric data, yet very few have examined the reasons for such differences. This paper contributes to the limited but growing body of literature that tracks inconsistent reports of hypertension using data from the Study on Global Ageing and Adult Health (SAGE). Focusing on five countries with different levels of development (Ghana, China, India, South Africa, and Russia), this study offers a comparative perspective that is missing in the literature. Data were obtained from wave 1 of SAGE collected in 2007/2008. A multinomial logit model was used to examine the effects of demographic and socioeconomic variables on the likelihood of respondents self-reporting that they are not hypertensive when their biometric data show otherwise. The authors also model the likelihood of respondents self-reporting that they are hypertensive when in fact their biometric data show they are not. Socioeconomic and demographic variables were shown to be significantly associated with inconsistent reporting of hypertension. For instance, it was observed that wealth was associated with a lower likelihood of self-reporting that one is not hypertensive when their biometric data indicate otherwise. Tracking such inconsistent reports is crucial to minimizing measurement errors and generating unbiased and more precise parameter estimates in hypertension research.
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 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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".