He said, she said: Work, biopsychosocial, and lifestyle contributions to coronary heart disease risk.
Bibliographic record
Abstract
OBJECTIVE: To test a model incorporating job characteristics, biopsychosocial, lifestyle, and nonmodifiable factors as they relate to coronary heart disease (CHD). Specifically, job characteristics and nonwork social ties (NWST) were examined as predictors of biopsychosocial health (BPSH), which was, in turn, expected to predict CHD directly and indirectly through influencing lifestyle. We also examined how age and family history of premature heart disease predicted objectively measured CHD risk. Within this model, sex differences were explored. METHOD: A structural equation modeling analysis of data from a cross-sectional sample of 541 employees (317 men and 224 women) taking part in a cross-organization workplace wellness program. T tests of sex differences were also conducted. RESULTS: Positive perceptions of job characteristics and NWST predicted positive BPSH. BPSH displayed no direct relationship to CHD risk, but positively predicted a healthier lifestyle. A healthier lifestyle was related to lower levels of CHD risk. Family history, but not age, was also useful in predicting CHD risk. Analyses indicated that men were significantly worse on all objective measures of CHD risk factors, but no other main effect sex differences were found. There were no differences between men and women in the relationships between variables. CONCLUSIONS: Adds to a body of literature indicating the importance of psychological components of the job in determining biopsychosocial health, and the importance of this variable in its impact on lifestyle decisions. The results support continued efforts to guide future interventions on lifestyle for both men and women.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".