Discharge interventions for First Nations people with a chronic condition or injury: a systematic review
Bibliographic record
Abstract
BACKGROUND: Aboriginal and Torres Strait Islander peoples have a unique place in Australia as the original inhabitants of the land. Similar to other First Nations people globally, they experience a disproportionate burden of injury and chronic health conditions. Discharge planning ensures ongoing care to avoid complications and achieve better health outcomes. Analysing discharge interventions that have been implemented and evaluated globally for First Nations people with an injury or chronic conditions can inform the implementation of strategies to ensure optimal ongoing care for Aboriginal and Torres Strait Islander people. METHODS: A systematic review was conducted to analyse discharge interventions conducted globally among First Nations people who sustained an injury or suffered from a chronic condition. We included documents published in English between January 2010 and July 2022. We followed the reporting guidelines and criteria set in Preferred Reporting Items for Systematic Review (PRISMA). Two independent reviewers screened the articles and extracted data from eligible papers. A quality appraisal of the studies was conducted using the Mixed Methods Appraisal Tool and the CONSIDER statement. RESULTS: Four quantitative and one qualitative study out of 4504 records met inclusion criteria. Three studies used interventions involving trained health professionals coordinating follow-up appointments, linkage with community care services and patient training. One study used 48-hour post discharge telephone follow-up and the other text messages with prompts to attend check-ups. The studies that included health professional coordination of follow-up, linkage with community care and patient education resulted in decreased readmissions, emergency presentations, hospital length of stay and unattended appointments. CONCLUSION: Further research on the field is needed to inform the design and delivery of effective programs to ensure quality health aftercare for First Nations people. We observed that discharge interventions in line with the principal domains of First Nations models of care including First Nations health workforce, accessible health services, holistic care, and self-determination were associated with better health outcomes. REGISTRATION: This study was prospectively registered in PROSPERO (ID CRD42021254718).
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".