Cost analysis of employing general practitioners within residential aged care facilities based on a prospective, stepped-wedge, cluster randomised trial
Bibliographic record
Abstract
Abstract Objective To assess the impacts of changing a model of care and employing general practitioners (GPs) within residential aged care facilities (RACFs) on costs to the aged care provider (ACP) and state and federal governments of Australia. Methods This study was a cost analysis of a prospective, stepped-wedge, cluster randomised trial. All financial data from the ACP for every RACF involved, before and after implementation of the new model were obtained. Costs of hospital transfers, admissions, ambulance usage and GP consultations were calculated. Costs of new infrastructure, recruiting and training new staff were accounted for. Costs were standardised to 2019 Australian Dollars per occupied bed day (OBD). Results Implementation of the new model of care resulted in overall cost savings of $9.7 per OBD to the ACP, with increased salary costs offset by increased federal government subsidies and Medicare claims income. Costs to the federal government increased by $19.6 per OBD, driven by increases in subsides. Costs savings of $3.0 per OBD to state governments were seen, driven by decreased costs of hospital transfers. Conclusions Implementation of a model of care including GPs employed at RACFs had a mixed impact on costs depending on perspective, with overall savings to the ACP and state government perspective.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".