Sense of coherence as a moderator of the effects of stressful life events on health
Bibliographic record
Abstract
STUDY OBJECTIVE: To test the hypothesis that Antonovsky's concept of sense of coherence (SOC) moderates (that is, buffers) the health impacts of stressful life events in a population based sample. DESIGN: Multiple linear and Poisson regression analyses of longitudinal data from a national survey of population health were used to examine the relations among SOC, the experience of recent stressful life events (for example, family breakdown, financial crisis, physical abuse), and two outcomes, self reported health status (SRH) and self reported number of physician visits during the previous year. SETTING: General population of Canada. PARTICIPANTS: Nationally representative sample of household residents aged 30 years of age or greater surveyed in 1998 and 2000 (n = 6505). MAIN RESULTS: After controlling for age, sex, and previous health status, a significant moderating effect (t = 2.24, p = 0.025) in the expected direction was found on respondents' SRH. The mean difference in SRH between those who did and did not experience a recent stressful life event was 0.24 (95% CI: 0.16, 0.32) in people with a below average SOC and 0.04 (95% CI: -0.04, 0.11) in people with a higher than average SOC score. The postulated moderating effect of SOC was not significant (t = 1.1, p > 0.05) in predicting the number of visits to a physician. CONCLUSIONS: SOC seems to buffer the impact of recent stressful life events on SRH.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".