Frequency versus time lost measures of absenteeism: Is the voluntariness distinction an urban legend?
Bibliographic record
Abstract
Summary We investigate a long‐standing methodological rule of thumb, the idea that the frequency of absenteeism from work approximates an expression of voluntary behavior while total time lost better reflects involuntary behavior and ill health. Conducting original meta‐analyses and using results from existing meta‐analyses, we determine that time lost and frequency are equally reliable, that the relationship between them approximates unity when corrections for measurement artifacts are applied, and that there is very little evidence for differential criterion‐related validity predicated on the voluntariness distinction. We supply new meta‐analytic estimates of the reliability of absenteeism adjusted for aggregation period and determine that most extant meta‐analyses of the correlates of absenteeism have markedly under‐corrected for unreliability. Our results question the basic construct validity of the time lost–frequency distinction, and they contradict the practice of using “trigger points” that factor absence frequency into attendance monitoring and associated discipline systems so as to discourage short‐term absenteeism, assumed to be volitional. We conclude that the idea that time lost and frequency reflect different degrees of voluntariness is an unsupported urban research legend. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.098 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 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".