Going in Circles: The Hidden Work of Hospital Staff Trying to Meet the Healthcare Needs of First Nations People Through “Patient-Centered Care”
Bibliographic record
Abstract
First Nations people in Australia have advocated for decades for improved healthcare experiences that meet their needs. Australian accreditation standards now require hospitals to undertake key activities and organizational changes aimed to improve healthcare for First Nations people, yet many hospitals remain unsure about how to best make these changes. I conducted an institutional ethnography (IE) for over two years in a large Australian hospital, drawing on qualitative interviews with 30 hospital staff, a policy document review, and participant observation. IE’s generous definition of work helped me see how the institutional undocumented and unsupported work undertaken by hospital staff members tries to make a health system that was not designed by or for First Nations people. Tracing these happenings led me to an ideological circle in which staff members’ work was simultaneously dismissed and subsumed under “patient-centered care” and was used to justify how no material change was needed. Conflicting institutional discourses perpetuated a virtual reality in which healthcare could be standardized for “all people” at the expense of First Nations people.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.034 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".