General practitioners and sickness certification for injury in Australia
Bibliographic record
Abstract
BACKGROUND: Strong evidence supports an early return to work after injury as a way to improve recovery. In Australia, General Practitioners (GPs) see about 96 % of injured workers, making them the main gatekeepers to workers' entitlements. Most people with compensable injuries in Australia are certified as "unfit to work" by their GP, with a minority of patients certified for modified work duties. The reasons for this apparent dissonance between evidence and practice remain unexplored. Little is known about the factors that influence GP sickness certification behaviour in Australia. The aim of this study is to describe the factors influencing Australian GPs certification practice through qualitative interviews with four key stakeholders. METHODS: From September to December 2012, 93 semi-structured interviews were undertaken in Melbourne, Australia. Participants included GPs, injured workers, employers and compensation agents. Data were thematically analysed. RESULTS: Five themes describing factors influencing GP certification were identified: 1. Divergent stakeholder views about the GP's role in facilitating return to work; 2. Communication between the four stakeholder groups; 3. Conflict between the stakeholder groups; 4. Allegations of GPs and injured workers misusing the compensation system and 5. The layout and content of the sickness certificate itself. CONCLUSION: By exploring GP certification practice from the perspectives of four key stakeholders, this study suggests that certification is an administrative and clinical task underpinned by a host of social and systemic factors. The findings highlight opportunities such as practice guideline development and improvements to the sickness certificate itself that may be targeted to improve GP sickness certification behaviour and return to work outcomes in an Australian context.
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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 | 0.004 | 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 | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".