Financial evaluation of interventions to reduce musculoskeletal disorder risk: A scoping review
Bibliographic record
Abstract
• More comprehensive information on financial returns of WMSD prevention is needed. • Most return on investment tools do not capture the complexity of WMSD aetiology. • Qualitative data needed to support understanding of return on investment. • Economic evaluations needed in a broader range of countries that currently available. Many interventions have aimed to reduce the incidence of work-related musculoskeletal disorders (WMSDs) which are a costly occupational health problem. However, information on the return on investment of these interventions is limited. This scoping review mapped published evidence of types of financial tools used to assess the return on investment on interventions to reduce WMSDs. The level within the organisation at which the intervention was targeted was also examined. PsycINFO, CINAHL, Web of Science and Embase were searched from 2000 to August 2023. Studies with financial evaluations of workplaces intervention/s to reduce WSMDs were included. Coding of financial tools, cost and benefit factors, and the level at which interventions were targeted was undertaken. Two review authors independently screened studies for inclusion. One author extracted data with review by a second author. Thirty-five articles met the inclusion criteria. Included studies were mostly from the US (n = 9), Canada (n = 8) and the Netherlands (n = 6). Cost-benefit, cost-effectiveness, cost-utility and return on investment approaches were used. Most commonly used cost factors included personnel, equipment, intervention costs, training, and consultant fees, and for economic benefits, productivity, absenteeism, and compensation. Current tools and approaches to economic evaluation do not take into account the likely efficacy of interventions and need to include a broader suite of cost and impact factors, based on known contributory factors such as exposure to psychosocial hazards and lead indicators such as reporting of musculoskeletal pain.
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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 | 0.036 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".