Improving outcomes for hospitalised First Nations peoples though greater cultural safety and better communication: The Communicate Study Partnership study protocol
Bibliographic record
Abstract
Abstract Background The Communicate Study is a partnership project which aims to transform the culture of healthcare systems to achieve excellence in culturally safe care for First Nations people. It responds to the ongoing impact of colonisation which results in First Nations peoples experiencing adverse outcomes of hospitalisation in Australia’s Northern Territory. In this setting, the majority of healthcare users are First Nations peoples but the majority of healthcare providers are not. Our hypotheses are that strategies to ensure cultural safety can be effectively taught, systems can become culturally safe, and that the provision of culturally safe healthcare in first languages will improve experiences and outcomes of hospitalisation. Methods We will implement a multi-component intervention at three hospitals over four years. The main intervention components are: cultural safety training called ‘Ask the Specialist Plus’ which incorporates a locally-developed, purpose-built podcast; developing a community of practice in cultural safety; and improving access to and uptake of Aboriginal language interpreters. Intervention components are informed by the ‘Behaviour Change Wheel’ and address a supply-demand model for interpreters. The philosophical underpinnings are Critical Race Theory, Freirean pedagogy and cultural safety. There are co-primary qualitative and quantitative outcome measures: cultural safety, as experienced by First Nations peoples at participating hospitals, and proportion of admitted First Nations patients who self-discharge. Qualitative measures of patient and provider experience, and patient-provider interactions, will be examined through interviews and observational data. Quantitative outcomes (documentation of language; uptake of interpreters (booked and completed); proportion of admissions ending in self-discharge; unplanned re-admission; hospital length of stay; costs and cost benefits of interpreter use) will be measured using time-series analysis. Continuous quality improvement will use data in a participatory way to motivate change. Program evaluation will assess reach, effectiveness, adoption, implementation and maintenance (‘RE-AIM’). Discussion The intervention components are innovative, sustainable and have been successfully piloted. Refinement and scale up through this project have the potential to transform First Nations patients’ experiences of care and health outcomes. Trial registration Registered with ClinicalTrials.gov Protocol Record 2008644
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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 | 0.063 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.050 | 0.011 |
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".