Bibliographic record
Abstract
I have just returned from the 5th International Rural and Remote Area Nurses Conference, which was organised through the ICN Rural and Remote Nurses Network. Held in Albury, Australia, from the 4th to the 6th March, approximately 90 nurses attended from Japan, Thailand, Canada, USA, New Zealand, and Australia. There were four keynote speakers—three from Australia and Dr. Judith Kulig from Canada. The concurrent sessions saw 51 papers presented. In addition, two symposia were presented, both by delegates from the USA and both focusing on disaster management. Without the work of the Australian Rural Nurses and Midwives (ARNM) Association, the conference would not have been the success it was. We were also grateful for the work of the Conference Organising Committee in Australia and the Abstract Committee located in the USA.The major theme arising from the conference was one of celebration of the work of rural nurses. From the student to the nurse practitioner, it was apparent that much was being done to ensure that rural nurses are prepared adequately for practice and, once in practice, will have the access they need to remain competent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.191 | 0.094 |
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".