Communication and collaboration among return-to-work stakeholders
Bibliographic record
Abstract
Purpose: Workers who are injured or become ill on the job are best able to return-to-work when stakeholders involved in their case collaborate and communicate. This study examined health care providers’ and case managers’ engagement in rehabilitation and return-to-work following workplace injury or illness.Method: In-depth interviews were conducted with 97 health care providers and 34 case managers in four Canadian provinces about their experiences facilitating rehabilitation and return-to-work, and interacting with system stakeholders.Results: A qualitative thematic content analysis demonstrated two key findings. Firstly, stakeholders were challenged to collaborate as a result of: barriers to interdisciplinary and cross-professional communication; philosophical differences about the timing and appropriateness of return-to-work; and confusion among health care providers about the workers' compensation system. Secondly, these challenges adversely affected the co-ordination of patient care, and consequentially, injured workers often became information conduits, and effective and timely treatment and return-to-work was sometimes negatively impacted.Conclusions: Communication challenges between health care providers and case managers may negatively impact patient care and alienate treating health care providers. Discussion about role clarification, the appropriateness of early return-to-work, how paperwork shapes health care providers’ role expectations, and strengthened inter-professional communication are considered.Implications for RehabilitationAdministrative and conceptual barriers in workers’ compensation systems challenge collaboration and communication between health care providers and case managers.Injured workers may become conduits of incorrect information, resulting in adversarial relationships, overturned health care providers’ recommendations, and their disengagement from rehabilitation and return-to-work.Stakeholders should clarify the role of health care providers during rehabilitation and return-to-work and the appropriateness of early return-to-work to mitigate recurring challenges.Communication procedures between health care specialists may disrupt these challenges, increasing the likelihood of timely and effective rehabilitation and return-to-work.
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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 | 0.026 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".