Validation of the 5-item workplace outcome suite on employee assistance programs for office workers
Bibliographic record
Abstract
To evaluate the effectiveness of Employee Assistance Program (EAP) interventions, the Workplace Outcome Suite (WOS) was developed. This study aims to determine if the new WOS 5-item version can be used to approximate the WOS 25-item version without excessive loss of reliability, validity, or sensitivity. A quantitative psychometric evaluation study design was employed. Secondary data analysis of the WOS 25-item questionnaire was conducted before and after EAP services were delivered to participants. This analysis used 2046 data responses from 1023 participants. Quantitative data analysis included descriptive statistics, Cohen’s d, paired t-test, the Wilcoxon signed-rank (non-parametric test), and bivariate factor analysis. Findings demonstrate that the WOS 5-item version can successfully detect changes in workplace functioning. Within all five constructs, users’ scores improved after EAP interventions, indicating improvement in mental health. Significant changes were detected for absenteeism, presenteeism, work engagement, and workplace distress. Bivariate correlation results indicate the WOS 5-item is a good representation of the 25-item version. There are strong correlations between each item on the WOS-5 and the corresponding items in each construct on the WOS-25. This evidence suggests the WOS 5-item version can be used to approximate the WOS 25-item version without excessive loss of reliability, validity, or sensitivity.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".