Is Optimism Associated with Physical Health? A Commentary on Rasmussen et al.
Bibliographic record
Abstract
In a recent meta-analysis, Rasmussen et al. [1] attempted to quantify the relationship between optimism and physical health. The authors reported an association between optimism and physical health of r = 0.17, which depended on whether subjective (r = 0.21) or objective (r = 0.11) physical health measures were used. These results were reported to be independent of whether studies were cross-sectional, longitudinal, or prospective (longitudinal with baseline control for physical health) or the type of covariates included. The authors concluded that optimism is a significant predictor of physical health outcomes and claimed that the inclusion of prospective studies bolstered the credibility of their findings and eliminated the possibility of reverse causality. We attempted to verify this claim by independently evaluating the prospective studies with objective measures of physical health used by Rasmussen et al. to estimate the optimism-physical health association. However, numerous apparent discrepancies between data reported in original articles and study design codes in Appendix 1 of Rasmussen et al. were found. Because of these apparent discrepancies and because the authors did not provide covariate adjustment codes, we contacted Dr. Rasmussen, who sent us a supplementary table with covariate adjustment, study design, and objective/subjective health outcome coding.
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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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.055 | 0.066 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".