Investigating differences in return to work for workers with work-related chronic pain and concurrent psychological injuries in British Columbia
Bibliographic record
Abstract
Work-related chronic pain injuries in British Columbia, Canada, have been on the rise, with a 48% increase in accepted workers’ compensation claims from 2017 to 2022. Similarly, psychological health claims have also risen, with a 51% increase in the number of claims submitted from 2019 to 2023. This growth is expected to continue in the coming years due to changes in workplace dynamics and an aging population. Previous research has shown that these injury types have a bidirectional relationship whereby having one increases an individual’s susceptibility to having the other. This study aims to investigate differences in time to return-to-work (RTW) among psychological, chronic pain, and psychological with chronic pain injuries and to identify the most influential factors affecting the likelihood of RTW for each injury type. Workers’ compensation claims data were used in this study to identify workers (n=414,507) with an accepted time-loss claim due to work-related psychological, chronic pain, psychological with chronic pain, or other injury types occurring between 2012 and 2019, with up to 3 years of follow-up. Descriptive statistics were used to analyze differences in time to RTW. Multivariable logistic regression was used to assess the role of workplace/organizational, socio-demographic, and injury-specific characteristics in determining the likelihood of RTW for each injury type. Results indicate substantial differences in days to RTW events after injury, with psychological with chronic pain injuries having the longest RTW durations (median=1095 days [Interquartile Range (IQR):1095 – 1095 days]), followed by chronic pain (median=1095 days [IQR:327 – 1095 days]) and psychological (median=399 days [IQR:67 – 1095 days]). All other injuries had a median RTW duration of 21 days [IQR:7 – 83 days]. Characteristics such as younger age, larger firm size, and having modified RTW increased the likelihood of RTW across injury types, while other variables had varying injury-specific effects. These findings suggest the need to modify current RTW programs to include better mental health and chronic pain management systems. Additionally, workers with concurrent psychological and chronic pain injuries warrant further investigation into how they can be further supported to promote earlier RTW outcomes, as over 75% of the cohort did not RTW within three years.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".