Knowledge translation lessons from an audit of Aboriginal Australians with acute coronary syndrome presenting to a regional hospital
Bibliographic record
Abstract
OBJECTIVE: Translation of evidence into practice by health systems can be slow and incomplete and may disproportionately impact disadvantaged populations. Coronary heart disease is the leading cause of death among Aboriginal Australians. Timely access to effective medical care for acute coronary syndrome substantially improves survival. A quality-of-care audit conducted at a regional Western Australian hospital in 2011-2012 compared the Emergency Department management of Aboriginal and non-Aboriginal acute coronary syndrome patients. This audit is used as a case study of translating knowledge processes in order to identify the factors that support equity-oriented knowledge translation. METHODS: In-depth interviews were conducted with a purposive sample of the audit team and further key stakeholders with interest/experience in knowledge translation in the context of Aboriginal health. Interviews were analysed for alignment of the knowledge translation process with the thematic steps outlined in Tugwell's cascade for equity-oriented knowledge translation framework. RESULTS: In preparing the audit, groundwork helped shape management support to ensure receptivity to targeting Aboriginal cardiovascular outcomes. Reporting of audit findings and resulting advocacy were undertaken by the audit team with awareness of the institutional hierarchy, appropriate timing, personal relationships and recognising the importance of tailoring messages to specific audiences. These strategies were also acknowledged as important in the key stakeholder interviews. A follow-up audit documented a general improvement in treatment guideline adherence and a reduction in treatment inequalities for Aboriginal presentations. CONCLUSION: As well as identifying outcomes such as practice changes, a useful evaluation increases understanding of why and how an intervention worked. Case studies such as this enrich our understanding of the complex human factors, including individual attributes, experiences and relationships and systemic factors that shape equity-oriented knowledge translation. Given the potential that improving knowledge translation has to close the gap in Aboriginal health disparities, we must choose strategies that adequately take into account the unique contingencies of context across institutions and cultures.
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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 | 0.094 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".