The Role of Personal Attributes in the Genesis and Progression of Lung Disease and Cigarette Smoking
Bibliographic record
Abstract
OBJECTIVES: We examined early maladaptive personal attributes (e.g., depression), later lung disease, and later maladaptive personal attributes over a significant part of a woman's life. METHODS: We gathered longitudinal data on a prospective cohort of community-dwelling women (n = 498) followed from young adulthood to late midlife. Results. We used structural equation modeling to assess the interrelations of maladaptive personal attributes, cigarette smoking, lung disease, and financial strain. The results supported a mediational model through which early maladaptive personal attributes were associated with smoking (b = 0.17, P < .001), which in turn predicted later lung disease (b = 0.33, P < .001), and lung disease was related to later family financial difficulties (b = 0.09, P < .05), which in turn were associated with later maladaptive personal attributes (b = 0.35, P < .001). CONCLUSIONS: Our results address a number of important public health and clinical issues. An understanding of the interrelations of smoking, underlying mental health conditions, financial stress, and later mental health conditions on the part of physicians and other health care providers can be critical in managing patients with lung disease.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".