The reliability, validity, and accuracy of self-reported absenteeism from work: A meta-analysis.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.019 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 it