A pre‐intervention benefit‐cost methodology to justify investments in workplace health
Bibliographic record
Abstract
Purpose While the rationale for interventions in a workplace to enhance employee health are well documented, practitioners have difficulty making an economic case to justify the investment required and to demonstrate positive returns on that investment. This paper aims to present case study data from an ergonomics evaluation of a call centre to demonstrate a simple, four‐step pre‐intervention methodology which provides an accounting‐based justification for funding workplace health‐related projects. Design/methodology/approach Physical and ergonomic assessments of the workplace and employee interviews establish health risk factors. Two direct (discretionary) costs and five indirect (non‐discretionary) operational costs are evaluated. The capital investment to implement the proposed workplace changes is determined. Total net identified benefits are established and used to create accounting‐based financial metrics. Findings Application of the methodology to the case study found worker compensation insurance, absenteeism and overtime wages to be neutral. Costs to train new workers, lost call processing time and cost of lost employee productivity were significant, the latter representing two‐thirds of the value of all potential benefits. Originality/value The paper creates accounting‐based metrics to mitigate health and safety risk factors, while identifying the potential for productivity gains. Management is provided with a simple decision tool to justify an investment in workplace changes.
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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 | 0.028 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".