A new clinical rating scale for work absence and productivity: validation in patients with major depressive disorder
Bibliographic record
Abstract
BACKGROUND: The prevalence of major depressive disorder (MDD) is highest in working age people and depression causes significant impairment in occupational functioning. Work productivity and work absence should be incorporated into clinical assessments but currently available scales may not be optimized for clinical use. This study seeks to validate the Lam Employment Absence and Productivity Scale (LEAPS), a 10-item self-report questionnaire that takes 3-5 minutes to complete. METHODS: The study sample consisted of consecutive patients attending a Mood Disorders outpatient clinic who were in full- or part-time paid work. All patients met DSM-IV criteria for MDD and completed during their intake assessment the LEAPS, the self-rated version of the Quick Inventory for Depressive Symptomatology (QIDS-SR), the Sheehan Disability Scale (SDS) and the Health and Work Performance Questionnaire (HPQ). Standard psychometric analyses for validation were conducted. RESULTS: A total of 234 patients with MDD completed the assessments. The LEAPS displayed excellent internal consistency as assessed by Cronbach's alpha of 0.89. External validity was assessed by comparing the LEAPS to the other clinical and work functioning scales. The LEAPS total score was significantly correlated with the SDS work disability score (r = 0.63, p < 0.01) and the Global Work Performance rating from the HPQ (r = -0.79, p < 0.01). The LEAPS total score also increased with greater depression severity. CONCLUSION: The LEAPS displays good internal and external validity in a population of patients with MDD attending an outpatient clinic, which suggests that it may be a clinically useful tool to assess and monitor work functioning and productivity in depressed patients.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".