The treating physicians experiences of the return to work process in Thunder Bay
Bibliographic record
Abstract
This study explored the experiences of physicians who treat injured workers (treating physicians) and are responsible for providing information to employers and the Workplace Safety and Insurance Board (WSIB) regarding an injured worker?s ability to return to work (RTW) following a workplace injury. This study specifically examined the factors that these treating physicians perceived as influential within the RTW process. \nSemistructured, face-to-face interviews were conducted during the early part of 2009 with nine general practitioners, four specialists (orthopaedic surgeons) and one physician with expertise in the field of disability management. Interviews were conducted with each treating physician individually in their clinical office. Interviews were recorded and professionally transcribed. A grounded theory approach was used to analyze the findings from these interviews. \nThe treating physicians identified several factors that I categorized into micro, meso, and macro level factors. At the micro level, the role of the treating physician and the treating physician?s view of an injured worker?s motivation to return to work and the perception of safe return-to-work emerged as important themes. At the meso level, communication as an overall theme with the frequency of communication, broken telephone syndrome, and facilitated return-to-work coordination as subthemes emerged. Further, at the macro level, the treating physicians revealed that the WSIB process, the social environment of the workplace, and the availability of health care services influenced their experiences with the RTW process and the medical management of the injured workers. \nThe treating physicians in this study described many challenges to medically managing injured workers following an occupational injury. Improvements were recommended in areas of physician remuneration, stakeholder collaboration, communication between parties, and facilitation of the RTW process. This study was the first of its kind to interview the treating physicians in a northern Ontario setting. Further research is needed to better understand the experiences of other stakeholders, including injured workers and other health care providers during the RTW process.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".