The quality of care delivered to residents in long-term care in Australia: an indicator-based review of resident records (CareTrack Aged study)
Bibliographic record
Abstract
BACKGROUND: This study estimated the prevalence of evidence-based care received by a population-based sample of Australian residents in long-term care (LTC) aged ≥ 65 years in 2021, measured by adherence to clinical practice guideline (CPG) recommendations. METHODS: Sixteen conditions/processes of care amendable to estimating evidence-based care at a population level were identified from prevalence data and CPGs. Candidate recommendations (n = 5609) were extracted from 139 CPGs which were converted to indicators. National experts in each condition rated the indicators via the RAND-UCLA Delphi process. For the 16 conditions, 236 evidence-based care indicators were ratified. A multi-stage sampling of LTC facilities and residents was undertaken. Trained aged-care nurses then undertook manual structured record reviews of care delivered between 1 March and 31 May 2021 (our record review period) to assess adherence with the indicators. RESULTS: Care received by 294 residents with 27,585 care encounters in 25 LTC facilities was evaluated. Residents received care for one to thirteen separate clinical conditions/processes of care (median = 10, mean = 9.7). Adherence to evidence-based care indicators was estimated at 53.2% (95% CI: 48.6, 57.7) ranging from a high of 81.3% (95% CI: 75.6, 86.3) for Bladder and Bowel to a low of 12.2% (95% CI: 1.6, 36.8) for Depression. Six conditions (skin integrity, end-of-life care, infection, sleep, medication, and depression) had less than 50% adherence with indicators. CONCLUSIONS: This is the first study of adherence to evidence-based care for people in LTC using multiple conditions and a standardised method. Vulnerable older people are not receiving evidence-based care for many physical problems, nor care to support their mental health nor for end-of-life care. The six conditions in which adherence with indicators was less than 50% could be the focus of improvement efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".