Self-assessed anaesthesia nursing competence and related factors
Bibliographic record
Abstract
Objective: Assessing the level of competence of nurses in anaesthesia care is important not only in ensuring the quality of anaesthesia care, but also in developing a competence-based nursing education programme. This study aimed to assess Finnish nurses’ competence in anaesthesia nursing and to describe factors associated with it. This study will provide knowledge to support a competence-based education approach to anaesthesia nursing.Methods: A cross-sectional research design was used. A self-assessment (Anaesthesia Nursing Competence Scale) was developed for this study. The scale (39 items, 7 domains) used a Visual Analogue Scale (0 = not competent at all, 100 = excellent). Data were collected from registered nurses (n = 222) in anaesthesia departments at university hospitals in Finland (May-October 2017).Results: The overall level of anaesthesia nursing competence was self-assessed as good (Mean 88, SD 9.0). Of the seven competence domains, collaboration within patient care was assessed as being the highest and knowledge of anaesthesia patient care the lowest. Longer work experience and completion of specialised anaesthesia nursing education were factors positively associated with anaesthesia nursing competence.Conclusions: This study suggests that the general nursing education of nurses should provide more opportunities to improve nurses’ competence in the theoretical knowledge of anaesthesia. A specialised programme of anaesthesia nursing education at a master’s level might be one suggestion to meet the challenges in anaesthesia nursing in Finland. Further studies with different data collection methods such as observation, a knowledge test, or patient interviews would provide a more extensive picture of anaesthesia nursing competence.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".