Measuring the Contribution of Workers' Health and Psychosocial Work‐Environment on Production Efficiency
Bibliographic record
Abstract
Increasingly many firms have started to implement programs intended to improve the workers' health and the psychosocial work‐environment, as well as other attributes of labor quality. Motivated by the need for evaluating to what extent the programs affect a firm's productivity performance, this study discusses a model for analyzing the contribution of labor quality attributes toward firm productivity. To assess the contribution from the labor quality attributes, we model firm productivity as the outcome of two separate processes within a firm: the physical production process and the labor quality process. Firm productivity is measured by a Malmquist‐like productivity index and is computed by Data Envelopment Analysis. Based on bootstrap methods we analyze potential statistical bias and provide bias‐corrected productivity estimates. The labor quality attributes are first modeled at an individual worker level as latent variables using Item Response Theory, and then aggregated to a firm‐level. The model is empirically validated using data from three manufacturing plants that participated in a coordinated worksite health promotion program. Over a 4‐year period (2000–2003), we observed a general improvement in efficiency of 2–5%, half of which could be attributed to an improvement in workers' health and psychosocial work‐environment. A key benefit with the model is that it is practical, easy to implement, and very fast to compute. The model also constructively contributes to the discourse on sustainability by providing a framework for deriving meaningful metrics and providing tangible measurements on the effect of sustainability‐related issues.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".