Bibliographic record
Abstract
[Extract] This special issue of Contemporary Nurse on Advances in Contemporary Indigenous Health Care has finally come to fruition. It has been a difficult and challenging journey, but the outcome has made it worthwhile. Importantly, this area of health care is one that deserves special attention and we commend the editors on the decision to once again make this a feature of the journal. The authors submitted a range of interesting and important papers covering issues of direct relevance to clinical health care, such as anti-smoking projects (Robertson, 2010; Thompson, 2010), issues surrounding access to health care services (Van Herk, Smith, & Andrew, 2010), nurses' experiences caring for circumcised patients (Mangena, Mulaudzi, & Peu, 2010), and other important areas such as education of nurses, ways to increase the numbers of Indigenous students (Biles & Biles, 2010; Meissner, 2010; Stuart & Nielsen, 2010; West, West, West, & Usher, 2010), and issues related to culture (Blackman, 2010; Downing & Kowal, 2010; Rigby et al., 2010). While most of the papers submitted were from Australian authors, we were very pleased to receive and accept two papers from international authors for the special edition (one from Canada and one from South Africa). Of course there were also three invited editorials from Australian nurse leaders, including an Indigenous nurse, a Professor of Nursing, and the Chief Nurse of Australia, which set the scene for the issue.
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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 | 0.002 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.682 | 0.363 |
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".