Poor respiratory health outcomes associated with high illness worry and alexithymia: Eleven-year prospective cohort study among the working-age population
Bibliographic record
Abstract
OBJECTIVE: Poor respiratory health outcomes have been associated with poorer physical health and higher psychological distress. The aim of this study was to investigate whether illness worry, alexithymia or low sense of coherence predict i) the onset of new respiratory disease, ii) respiratory symptoms or iii) lung function among the working-age population, independently of comorbidity mood-, anxiety, or alcohol abuse disorders. METHODS: The study was conducted among a nationally representative sample of the Finnish population (BRIF8901) aged 30-54 years (N = 2310) in 2000-2001 and was followed up in 2011. Individuals with a diagnosed respiratory disease or a severe psychiatric disorder at baseline were excluded. Lung function was measured by a spirometry test and psychiatric disorders were diagnosed using a structured clinical interview. Structured questionnaires were used to measure self-reported respiratory symptoms and diseases, illness worry, alexithymia, and sense of coherence. RESULTS: High illness worry predicted an 11-year incidence of asthma (OR 1.47, 95% CI 1.09-1.99, p = 0.01). Alexithymia predicted shortness of breath (OR 1.32, 95% CI 1.13-1.53, p < 0.01) and 11-year incidence of COPD (OR 2.84, 95% CI 1.37-5.88, p < 0.01), even after several adjustments for physical and mental health. Psychological dispositions did not associate with lung function in 2011. CONCLUSIONS: In the general population, psychological factors that modify health behaviour predicted adverse respiratory health outcomes independently of lung function after 11 years of follow-up. This indicates that considering them part of personalized treatment planning is important for promoting health-related behaviour among the working-age population.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".