The Role of Healthcare Providers in Return to Work
Bibliographic record
Abstract
International research has generated strong evidence that healthcare providers (HCPs) play a key role in the return to work (RTW) process. However, pressure on consultation time, administrative challenges and limited knowledge about a patient's workplace can thwart meaningful engagement. Aim: Our study sought to understand how HCPs interact with workers compensation boards (WCBs), manage the treatment of workers compensation patients and navigate the RTW process. Method: The study involved in-depth interviews with 97 HCPs in British Columbia, Manitoba, Ontario and Newfoundland and Labrador and interviews with 34 case managers (CMs). An inductive, constant comparative analysis was employed to develop key themes. Findings: Most HCPs did not encounter significant problems with the workers compensation system or the RTW process when they treated patients who had visible, acute, physical injuries, but faced challenges when they encountered patients with multiple injuries, gradual-onset or complex illnesses, chronic pain and mental health conditions. In these circumstances, many experienced the workers compensation system as opaque and confusing. A number of systemic, process and administrative hurdles, disagreements about medical decisions and lack of role clarity impeded the meaningful engagement of HCPs in RTW. In turn, this has resulted in challenges for injured workers (IWs), as well as inefficiencies in the workers compensation system. Conclusion: This study raises questions about the appropriate role of HCPs in the RTW process. We offer suggestions about practices and policies that can clarify the role of HCPs and make workers compensation systems easier to navigate for all stakeholders.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 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".