Is clinician refusal to treat an emerging problem in injury compensation systems?
Bibliographic record
Abstract
OBJECTIVE: The reasons that doctors may refuse or be reluctant to treat have not been widely explored in the medical literature. To understand the ethical implications of reluctance to treat there is a need to recognise the constraints of doctors working in complex systems and to consider how these constraints may influence reluctance. The aim of this paper is to illustrate these constraints using the case of compensable injury in the Australian context. DESIGN: Between September and December 2012, a qualitative investigation involving face-to-face semistructured interviews examined the knowledge, attitudes and practices of general practitioners (GPs) facilitating return to work in people with compensable injuries. SETTING: Compensable injury management in general practice in Melbourne, Australia. PARTICIPANTS: 25 GPs who were treating, or had treated a patient with compensable injury. RESULTS: The practice of clinicians refusing treatment was described by all participants. While most GPs reported refusal to treat among their colleagues in primary and specialist care, many participants also described their own reluctance to treat people with compensable injuries. Reasons offered included time and financial burdens, in addition to the clinical complexities involved in compensable injury management. CONCLUSIONS: In the case of compensable injury management, reluctance and refusal to treat is likely to have a domino effect by increasing the time and financial burden of clinically complex patients on the remaining clinicians. This may present a significant challenge to an effective, sustainable compensation system. Urgent research is needed to understand the extent and implications of reluctance and refusal to treat and to identify strategies to engage clinicians in treating people with compensable injuries.
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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 | 0.067 | 0.255 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.013 | 0.011 |
| 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".