Development and Testing of a Measure of Self-awareness Among Nurses
Bibliographic record
Abstract
Self-awareness is an essential nursing competency and there is limited knowledge about nurses' levels and application of self-awareness and instruments to measure nursing-specific self-awareness. Using mixed methods, we developed and tested a scale to measure nurses' self-awareness. First, 13 nurses were interviewed to understand their meanings of self-awareness and to develop nursing-specific self-awareness scale. Qualitative analysis generated professional, personal, contextual, and contentious aspects of self-awareness. Second, a 25-item scale assessed through expert consultations and pilot testing with 252 nurses. The content validity index was 0.94. After psychometric testing, seven items were deleted. Cronbach's alpha for the 18-item scale was 0.87 and the four-factor structure accounted for 45.55% of the variance. Lastly, the final scale was administered to 216 nurses. Nurses' had moderate self-awareness (59.65 ± 7.01), significantly associated with age and years of the clinical and educational experience. Intensive care nurses were more self-aware than nurses in other settings.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".