ORNAC 23rd National and IFPN International Conference: International alliance for perioperative best practice: The Ottawa convention centre, Ottawa, Ontario, Canada, 21-25 April 2013
Bibliographic record
Abstract
It was a privilege to attend this year’s joint Operating Room Nurses Association of Canada (ORNAC) and International Federation of Perioperative Nurses (IFPN) Conference hosted in Ottawa, Canada on 21–25 April 2013 to present on my research findings via my paper: “Perioperative Nurses: Finding Meaning from their Experiences in Multi-Organ Procurement Surgery”. After presenting my preliminary PhD research findings at the previous ORNAC Conference, I vowed to return and present my final PhD findings at a future ORNAC Conference. I submitted my abstract; however, little did I know that I would be expecting my fourth child and racing to complete my PhD before the baby’s arrival. When I received notification my paper had been accepted this caused some dilemmas as to how I was going to get there, leave a small baby and fulfil my obligation to present my PhD findings. The solution was a quick visit, several interconnecting flights to get to the conference, stay for a couple of days, present my paper and return home as quickly as possible.
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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.116 | 0.024 |
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".