Keeping Vigil over the Patient: a grounded theory of nurse anaesthesia practice
Bibliographic record
Abstract
AIM: This paper is a report of a study exploring the role and practice of nurse anaesthetists, with particular attention to describing how it is 'nursing'. BACKGROUND: In many countries, there is no nurse anaesthetist role. Recent events suggest that hesitancy about the role may be changing in Canada. Yet, there is limited understanding in Canada about nurse anaesthesia, and many nurses do not believe it is nursing. METHOD: This grounded theory study involved theoretical and purposive sampling to gather data through: (i) participant observation and face-to-face interviews at the 75th annual American Association of Nurse Anesthetists convention in August, 2006, (ii) follow-up telephone interviews and (iii) a 4-day site visit to a small city in the west of the United States to observe anaesthetists in practice and working with students, visit an educational program, and conduct further interviews. Data collection and analysis were iterative and continued until saturation was reached in December, 2007. FINDINGS: A basic social process of how nurse anaesthetists practise, Keeping Vigil over the Patient, was identified. Keeping Vigil over the Patient is comprised of four categories: Engaging with the Patient, Finessing the Human-Technology Interface, Massaging the Message and Foregrounding Nursing. CONCLUSION: Nursing was clearly evident in anaesthesia practice, reflecting a seamless integration of divergent ontological perspectives. Based on this examination, this role has considerable potential as a new advanced practice nursing role in countries where it has not yet been adopted.
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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 | 0.036 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".