Discharge Interventions for First Nations People with Injury or Chronic Conditions: A Protocol for a Systematic Review
Bibliographic record
Abstract
Severe injury and chronic conditions require long-term management by multidisciplinary teams. Appropriate discharge planning ensures ongoing care to mitigate the long-term impact of injuries and chronic conditions. However, First Nations peoples in Australia face ongoing barriers to aftercare. This systematic review will locate and analyse global evidence of discharge interventions that have been implemented to improve aftercare and enhance health outcomes among First Nations people with an injury or chronic condition. A systematic search will be conducted using five databases, Google, and Google scholar. Global studies published in English will be included. We will analyse aftercare interventions implemented and the health outcomes associated. Two independent reviewers will screen and select studies and then extract and analyse the data. Quality appraisal of the included studies will be conducted using the Mixed Methods Appraisal Tool and the CONSIDER statement. The proposed study will analyse global evidence on discharge interventions that have been implemented for First Nations people with an injury or chronic conditions and their associated health outcomes. Our findings will guide healthcare quality improvement to ensure Aboriginal and Torres Strait Islander peoples have ongoing access to culturally safe aftercare services.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".