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 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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".