“Taking our blindfolds off”: Acknowledging the vision of first nations peoples for nursing and midwifery
Bibliographic record
Abstract
This editorial responds to a recent reminder from an Elder to acknowledge and respect First Nations ways of knowing, doing, and being as health professionals and researchers. This reminder asked us to critically reflect on our professional stance and practices as nurses, midwives and researchers in the light of the fire that still burns at the Aboriginal tent Embassy and recent dialogues for Australia Day. In light of the international Black Lives Matter movement in 2020, we discuss the importance of our shared roles and responsibilities to continue to challenge racism and oppressive practices in Australian health care. Decolonising nursing and midwifery practice, policy, research, and education approaches offer a clear transformational reform process to address oppressive practices and racism including attitudes, ignorance and bias, generalisations, assumptions, uninformed opinions and commit to developing and embedding cultural safety in the nursing and midwifery profession.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.022 |
| 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".