The reliability, validity, and accuracy of self-reported absenteeism from work: A meta-analysis.
Bibliographic record
Abstract
Because of a variety of access limitations, self-reported absenteeism from work is often employed in research concerning health, organizational behavior, and economics, and it is ubiquitous in large scale population surveys in these domains. Several well established cognitive and social-motivational biases suggest that self-reports of absence will exhibit convergent validity with records-based measures but that people will tend to underreport the behavior. We used meta-analysis to summarize the reliability, validity, and accuracy of absence self-reports. The results suggested that self-reports of absenteeism offer adequate test-retest reliability and that they exhibit reasonably good rank order convergence with organizational records. However, people have a decided tendency to underreport their absenteeism, although such underreporting has decreased over time. Also, self-reports were more accurate when sickness absence rather than absence for any reason was probed. It is concluded that self-reported absenteeism might serve as a valid measure in some correlational research designs. However, when accurate knowledge of absolute absenteeism levels is essential, the tendency to underreport could result in flawed policy decisions.
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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 | 0.030 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.020 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".