Assessing the caring behaviors of critical care nurses
Bibliographic record
Abstract
Objective: To assess the critical care nurses’ perception of their caring behaviors and factors affecting these behaviors.Methods: Participants of this descriptive correlational exploratory study included 277 critical care nurses selected conveniently from nurses worked in all critical care units in King Khalid Hospital, Jeddah. A self-reported questionnaire namely, “Critical Care Nurses Caring Behavior Perception” developed by the researchers after reviewing related literature was used to assess caring behaviors and their affecting factors as perceived by critical care nurses.Results: Seventy percent of the nurses aged between 31 to 50 years old and more than half of nurses had ICU experience ranged from 6 to 10 years, while two thirds of nurses had no previous training about caring behaviors. The study findings revealed that the majority of nurses had high scores of perceived caring behaviors, whereas the mean of their perception was 296.96 ± 18.32. There was a statistical significant positive relationship between nurses’ perception and their work circumstances, workload, job satisfaction, educational background and patient characteristics.Conclusions: It is important to consider critical units’ circumstances, nurses’ educational background, job satisfaction, as well as the nature of critically ill patients in order to promote nurses awareness and implementation of caring behaviors. Moreover, replication of the current study using qualitative approach for in-depth analysis of the impact of factors could affecting caring behaviors on nurses’ perception in various highly specialized critical care units.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".