Psychiatric care implications of the Aged Care Royal Commission: Putting the cart before the horse
Bibliographic record
Abstract
That another Royal Commission (RC) or Board of Inquiry has eventuated is sadly unremarkable, given the breadth and depth of dysfunction in Australia’s aged care system. The key is whether there will be any effective action, as the accretion of problems through decades of manifest neglect will be very difficult to remediate. Substantial reform of the Australia’s Aged Care system has been proposed and is welcomed (Commonwealth of Australia, 2021). There is a great deal that could be achieved with concerted effort. However, in common with the recent Productivity Commission Report on Mental Health, there are concerns about the recommendations directly affecting clinical healthcare (Looi et al., 2021). Therefore, we focus on the specific recommendations that are of most direct relevance to the provision of psychiatric care for older Australians, discussing the proposals and their ramifications.\nThe metaphor of putting the cart before the horse is apt. The substantive redesign of the aged care system, especially in relation to improved and coordinated clinical governance, provision of funding, infrastructure and, crucially, workforce capacity, is essential before the implementation of recommendations directly impacting clinical care.\nAustralia clearly needs better availability and improved quality of mental health care in the aged care sector. However, clinically relevant recommendations appear to lack a practical understanding of the real-world context of provision of psychiatric care in Australia’s aged care system and systemic issues, especially in residential aged care facilities (RACFs) (Looi and Macfarlane, 2014). The key concerns relate to the practicalities of implementation of wide-ranging multi-level inter-governmental funding and policy in relation to specialist psychiatric care; the lack of consideration of the workforce planning, training, retention and recruitment necessary to provide specialist psychiatric care; and potential unexpected consequences of the restrictions on pharmacological and non-pharmacological treatment that are proposed in advance of essentially addressing the two former matters. In particular, there is burgeoning evidence for the efficacy of nonpharmacological psychosocial and nursing strategies, and these should be the basis of mental health care in RACFs. However, the staffing levels in RACFs and mental health skills of most carers (nursing assistants, enrolled nurses, personal carers) may be inadequate to implement such strategies. (Looi and Macfarlane, 2014)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".