Using specification curve analysis to explore prospective associations between dimensions of positive psychological well-being and cardiometabolic disease.
Bibliographic record
Abstract
OBJECTIVE: Literature suggests that higher positive psychological well-being (PPWB) is associated with reduced risk of cardiometabolic disease. However, PPWB is multidimensional. Most models do not distinguish between dimensions of PPWB in relation to cardiometabolic disease. This study demonstrated how specification curve analysis can be used to explore if the association between PPWB and incident cardiometabolic disease is influenced by the dimension of PPWB, cardiometabolic disease, and covariates under investigation. METHOD: = 2,895). Nine dimensions of PPWB and covariates were measured at baseline (2004-2005) and five cardiometabolic diseases were self-reported at follow-up (2013-2014). One hundred eighty model specifications, each containing one dimension of PPWB, one cardiometabolic disease, and one set of covariates, were generated. Standardized odds ratios from corresponding logistic regression models, in which PPWB predicted incident cardiometabolic disease, were then plotted on a specification curve. RESULTS: The median standardized odds ratio across models was 0.94. PPWB was inversely associated with incident cardiometabolic disease in 18% of models. Significant associations depended upon the dimension of PPWB, the outcome, and covariates. CONCLUSION: Researcher decisions about the dimension of PPWB, cardiometabolic disease outcome, and covariates under investigation appear to be consequential. Specification curve analysis can be used to develop an evidence base that starts to distinguish between dimensions of PPWB in relation to cardiometabolic disease. Thinking carefully about if and how specific dimensions of PPWB are associated with particular health outcomes is an avenue for theory refinement. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.076 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".