Evaluation of a pathway to address take own leave events for First Nations peoples presenting for emergency care: The Deadly <scp>RED</scp> project
Bibliographic record
Abstract
OBJECTIVE: The 'Deadly RED' project primarily aimed to improve culturally competent care to reduce the number of First Nations patients presenting to a Queensland ED who 'Take own leave' (TOL). The secondary aim was to evaluate the implementation project. METHODS: A pre/post-test quasi experimental study design using mixed methods was co-designed with adherence to Indigenous research considerations. Quantitative analysis of First Nations presentations before and after Deadly RED implementation was performed using SPSS. Qualitative analysis of transcribed research yarns in NVIVO was coded and themed for analysis. Staff experiences and perspectives were collated using electronically distributed surveys and process audits were performed. RESULTS: A total of 1096 First Nations presentations June to August 2021 and 1167 in the matched 2022 post-implementation period were analysed. Significantly more patients were recorded as TOL post-implementation (13.0% pre vs 21.3% post) and representations rates were unchanged. Forty-six staff surveyed identified improvements in all parameters including cultural appropriateness and quality of care. Qualitative analysis of 85 research yarns revealed themes migrated to increasingly acceptable, accessible, and usable care. Notably, 45% of the First Nation's patients recorded as TOL self-reported that their treatment was complete. The study was feasible as 80% of packs distributed and 73% follow-up screening after TOL. CONCLUSIONS: The Deadly RED evaluation revealed significant discrepancies in the reported data points of TOL and the 'story' of the First Nations persons experience of appropriate and completed care. Staff awareness and cultural capability improved significantly, and yarning allowed knowledge translation and improvements in communication which contributed to a better healthcare experience for First Nations patients attending our ED.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".