Bibliographic record
Abstract
Facilitating the future of small rural hospitals I'm not a rural person.I was born in Sydney; I now live in Melbourne.I've never lived in a small town, so I feel like somewhat of a fraud talking about the future of small rural hospitals.In the past, my attitude towards rural health care could be characterised as benign neglect, with an important exception I'll come to.For some of my career, I was responsible for budgets and budget savings in particular.My view then was that the big money is in the big hospitals, so I didn't look to rural closures or amalgamations to solve budget deficits.This is still my view.Almost a decade ago, I was asked to lead a review of quality and safety in Victorian hospitals following tragic outcomes at Bacchus Marsh Hospital associated with poor clinical governance.As part of that review, I was forced to think more carefully about the trade-offs involved in rural health provision, between access, the workforce and clinical governance challenges, and the broader role of hospitals that I will talk about later.Victoria has seen a flurry of amalgamation talk over the last year with on again-off again-on again oscillations favouring mergers either forced or voluntary.There are good reasons to argue for amalgamations-particularly those that are voluntary-as they can create improvements for both staff and communities in rural Victoria as our Grampians Health case study shows. 1 Money is not the only reason to look to amalgamations, care quality is another and my observation-based on anecdote only I'm afraid-is that there are significant weaknesses in clinical governance in some small hospitals that need to be addressed.Part-time, advisory medical administrative oversight, especially without clear and transparent lines of accountability, has been shown to be a recipe for disaster (Medical Board of Australia v Dr. Gruner (Review and Regulation) (2022) VCAT 1116; Medical Board of Australia v Dr. Gruner (Review and Regulation) (2023) VCAT 273).Medical practitioners in some cases are able to hold small communities and their hospitals to ransom.But I think the obsession with structural solutions is not the place to start.The critical issue to address is workforce, and not enough is being done about this.Secondly, and what I want to focus most of this talk on, is thinking through what a small rural hospital is, as we move into the second quarter of this century.The failure to fully understand the role that small hospitals play contributes to muddled policy thinking and poor policy prescriptions.were generated or analysed during the current study.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".