The Benefits of Cooperative Inquiry in Health Services Research: Lessons from an Australian Aboriginal and Torres Strait Islander Health Study
Bibliographic record
Abstract
Health services research is underpinned by partnerships between researchers and health services. Partnership-based research is increasingly needed to deal with the uncertainty of global pandemics, climate change induced severe weather events, and other disruptions. To date there is very little data on what has happened to health services research during the COVID-19 pandemic. This paper describes the establishment of an Australian multistate Decolonising Practice research project and charts its adaptation in the face of disruptions. The project used cooperative inquiry method, where partner health services contribute as coresearchers. When the COVID-19 pandemic hit, data collection needed to be immediately paused, and when restrictions started to lift, all research plans had to be renegotiated with services. Adapting the research surfaced health service, university, and staffing considerations. Our experience suggests that cooperative inquiry was invaluable in successfully navigating this uncertainty and negotiating the continuance of the research. Flexible, participatory methods such as cooperative inquiry will continue to be vital for successful health services research predicated on partnerships between researchers and health services into the future. They are also crucial for understanding local context and health services priorities and ways of working, and for decolonising Indigenous health research.
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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 | 0.351 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.030 | 0.028 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".