Costs of Musculoskeletal Injury in the California Film and Motion Picture Industry
Bibliographic record
Abstract
INTRODUCTION: Musculoskeletal injuries may have a significant economic impact on the film and motion picture (FMP) industry. However, there is currently no comprehensive data on the cost of workers’ compensation (WC) claims in the FMP industry. We present the first analysis of the cost of musculoskeletal injuries in the California (CA) FMP industry. METHODS: We reviewed the WC claims database of the Workers’ Compensation Insurance Rating Bureau of California (WCIRB) from 2003 to 2007 and employment statistics through the US Bureau of Labor Statistics (BLS). We analyzed the medical cost and indemnity of musculoskeletal injuries and compared the CA FMP injury data to the data for all CA industries. RESULTS: From 2003-2009, the total cost of WC claims in the CA FMP industry was $19.1 million per year, 88.6% of which was attributed to musculoskeletal injuries. The anatomical sites which incurred the most expense were the knee, lower back, and ankle at $2.3, $1.5 and $1.1 million per year, respectively. The most expensive causes of injury were work-directed activity and falls, totaling $5.4 and $4.7 million per year, respectively. The most costly types of isolated injuries were dislocations and fractures at $57,000 and $55,000 per claim. Additionally, the average cost per anatomic site, cause of injury and type of injury were significantly different for the CA FMP compared to CA industry in general. Over the course of the seven years that data was reviewed, orthopedic injury cost $191.71 per worker per year while orthopedic injury cost $224.00 per worker per year across CA industries (p<0.001). CONCLUSION: Musculoskeletal injuries contribute substantially to both FMP expenditures and US WC costs. Though the costs for injuries were statistically significant between the FMP and CA industries, the clinical significance has yet to be seen. The data presented in this study provides detailed data to help guide future designs for reducing costs associated with workplace injury in both the FMP industry and across CA industries.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".