Managing injured workers: family physicians' experiences.
Bibliographic record
Abstract
OBJECTIVE: To understand family physicians' experiences in managing patients within the workers' compensation system. DESIGN: Qualitative study using a phenomenologic approach. SETTING: London and surrounding communities in southwestern Ontario. PARTICIPANTS: Family physicians working in community-based and academic practices. METHOD: In-depth interviews conducted between February and May 2001 with a maximum variation sample of 10 family doctors. MAIN FINDINGS: Few participants enjoyed dealing with workers' compensation problems. Despite the generally straightforward nature of most work related to musculoskeletal injuries, management had to take place within the perceived complexities of the return-to-work process. Suspicion, isolation, and frustration characterized experiences with care of persisting, ill-defined, or complex conditions. Challenged by lack of time, participants were wary when dealing with employers and especially concerned about patient confidentiality. Hence, workplace communication seldom extended beyond the use of standard workers' compensation forms. While appreciative of the input of other professionals within the workers' compensation system, family practitioners were suspicious of external influences on clinical decision making. Participants' perceived commitment to patients conflicted with insurer requirements for adherence to guidelines and pathways of care. Even when patient-doctor relationships were challenged by the effects of an injury, participants saw a clear advantage in maintaining these relationships as a base for future care. CONCLUSION: Although family doctors are integral to management of Canadians with work-related injuries, our findings highlight the complexities of that care. Primary occupational health care extended beyond treatment of injuries into domains of intersectoral communication and patient-doctor relationships. Our findings suggest that workers' compensation authorities could benefit from a better understanding of the dynamics of contemporary family practice and particularly of time and cost barriers to workplace liaison. Communicating with employers would be less threatening if there were an explicit organizational strategy designed to allay family practitioners' anxieties about whether direct liaison with employers is inappropriate advocacy, a compromise to confidentiality, or good industrial practice.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".