Bibliographic record
Abstract
CONTEXT: Mental health care for Australian Aboriginal and Torres Strait Islander people living in rural and remote locations is delivered primarily by nurses. Culturally safe therapeutic interventions can promote understanding and improve care. Reflective knowledge thinking, writing and practice can support nurses to practice cultural safety. ISSUES: Two instances of mental health care for Australian Aboriginal and Torres Strait Islander clients are described in this reflective piece of writing. The care provided in both instances was culturally inappropriate and/or inadequate. I was an agent or observer in both cases, which happened during my employment as a mental health nurse in Australia. The first story, 'the traumatisation of Client A' describes an instance where I, from a place of ignorance, acted without cultural sensitivity and knowledge. I restrained and observed a client in a way that accorded with workplace policy but, at the same time, failed to take into account the circumstances and cultural safety of my client. The second story, 'the misunderstandings about Client B', occurred much later in my career. This time, I engaged with the client, acted with cultural safety, listened to his story and was able to clear up misunderstandings surrounding his presentation to hospital. LESSONS LEARNED: The two events described in this article led me to discover the nurse I was then and the nurse I have become now. In comparing the two events and my level of awareness and understanding of Aboriginal peoples, along with my own actions, I reflect on my own journey of discovery, which has informed and shaped my awareness as a culturally safe and more sensitive nurse.
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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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.047 | 0.043 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.007 | 0.031 |
| Research integrity | 0.010 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 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".