Using anchoring vignettes to assess the comparability of self‐rated feelings of sadness, lowness or depression in France and Vietnam
Bibliographic record
Abstract
General measures of self-rated health are collected routinely in national health surveys and widely used in the analyses of determinants of health and health care utilization. However, these subjective assessments can be influenced by health expectations (contextualized beliefs about health) that may vary systematically across individuals and be associated with their socio-demographic characteristics. Our objective is to contrast the impact of health expectations associated with respondent characteristics (reporting heterogeneity) on self-rated feelings of sadness, lowness or depression obtained from general population samples in France and Vietnam. Based on self ratings and ratings in response to common anchoring vignettes depicting different levels of depression, we used nationally representative data from the World Health Survey conducted in France (2002) and Vietnam (2002-2003) and a modification of the standard probit model to test and adjust for reporting heterogeneity associated with individual characteristics. We find evidence of reporting heterogeneity within France and Vietnam and across the two countries. In particular we find that, when adjusted for reporting heterogeneity, sex is no longer significantly associated with self-rated feelings of sadness, lowness or depression in France. Given the absence of clear biological markers in the definitions of depressive disorders and the substantial impact reporting heterogeneity is shown to have, measures of depressive disorders based on self-reports should be interpreted with caution.
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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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".