Sickness presence, sickness absence, and self‐reported health and symptoms
Bibliographic record
Abstract
Purpose The purpose of this paper is to compare sickness presence (SP) and sickness absence (SA) regarding the strength of their relationship to health/ill‐health. In a previous Canadian study a stronger association between SP and health/ill‐health than between SA and health/ill‐health was shown. Design/methodology/approach Five Swedish data sets from the years 1992 to 2005 provided the study populations, including both representative samples and specific occupational groups ( n =425‐3,622). Univariate correlations and multiple logistic regression analyses were performed. The data sets contained questions on SP and SA as well as on various health complaints and, in some cases, self‐rated health (SRH). Findings The general trend was that correlations and odds ratios increased regularly for both SP and SA, with SP showing the highest values. In one data set, SRH was predicted by a combination of the two measures, with an explained variance of 25 percent. Stratified analyses showed that the more irreplaceable an individual is at work, the larger is the difference in correlation size between SP and SA with regard to SRH. SP also showed an accentuated and stronger association with SRH than SA among individuals reporting poor economic circumstances. Practical implications The results support the notion that SA is an insufficient, and even misleading, measure of health status for certain groups in the labor market, which seem to have poorer health than the measure of SA would indicate. Orginality/value A combined measure of sickness presence and absence may be worth considering as an indicator of both individual and organizational health status.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".