Duration of work disability: A comparison of self-report and administrative data
Bibliographic record
Abstract
BACKGROUND: Studies have used insurer-reported compensable days absent as an outcome measure when studying work-related injury or illness. Compared to self-reported days absent, insurer data are less expensive to collect. Previous work has identified that insurer-claims data consistently underestimate the duration of days absent when compared to self-report. The objective of this study was to examine the agreement between the number of self-reported days absent from work following a compensable work-related injury and the number of insurer-reported compensation days paid, and to examine factors associated with the magnitude of the discrepancy between the number of self-reported days absent and the number of insurer-reported compensated days paid. METHODS: One hundred sixty six respondents who experienced a work-related injury were interviewed approximately 200 days post-injury to assess the number of days absent from work. The number of days compensated by the insurer was compared to self-report using descriptive statistics and linear regression. RESULTS: Respondents who had yet to experience a return-to-work (RTW) had the largest median discrepancy followed by respondents with an unsustained RTW and finally sustained RTW. Respondents with upper extremity injuries, lower education, and lower RTW self-efficacy showed greater discrepancy between self-reported and compensated days absent. Among respondents who self-reported fewer days absent than insurer-compensated days absent an inverse relationship between firm size and discrepancy was noted. CONCLUSIONS: Researchers should be aware of the discrepancies between self-reported and compensated days absent. Future studies planning to incorporate days absent as an outcome variable should carefully consider what measure would be more appropriate and potentially collect both self-report and administrative data to assess the discrepancy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".