Alberta Novice Critical Care Nurses’ Learning Styles, Self-Efficacy, and Perceptions of Their Orientation Program
Bibliographic record
Abstract
Background: All Registered Nurses that have no previous adult intensive care unit (ICU) experience who are hired into an ICU in Alberta, must go through a mandatory orientation program. Due to the limited research investigating ICU orientation programs and learning styles for critical care nurses, it is valuable to develop an understanding of how the Alberta ICU orientation program might be improved for novice critical care nurses (NCCNs). Research Questions: 1) What is the primary learning style of NCCNs in Alberta? 2) What are the perceived strengths and gaps of the critical care nursing orientation program among NCCNs in Alberta? Method: A descriptive, cross-sectional survey of NCCNs (n = 60) in Alberta was conducted. The target population was Registered Nurses in Alberta who have worked in an adult ICU less than 3 years and had no prior experience working in ICU. Results: The respondents’ strongest learning style preference was for sensing (32%) and balanced (30%) learning styles. Most respondents preferred the practicum/preceptorship (63.5%) portion of their orientation program the most and would prefer to have more simulation (55.8%) in their orientation. Respondents reported that their orientation program was too short; they would like to have a longer orientation for additional teaching and practice related to critical care medications, emergency patient situations, and teamwork. The ICU staff, including nurses, educators, physicians, and managers, had the greatest influence on respondents’ orientation program and transition into ICU. Conclusion: The primary learning preference for respondents was sensing suggesting NCCNs are concrete thinkers, who tend to be better at memorizing facts, enjoy trying things out, and engaging in hands-on experiences. Other respondents had sensing and balanced learning style preferences, thus using multiple teaching methods will aid in meeting learners needs and create optimal teaching opportunities. Healthy work environment, trained preceptors, and welcoming co-workers all positively affect NCCNs orientation and transition into ICU. To support nurses new to ICUs, it is important for organizational and nursing leaders to recognize the multiple factors that can influence NCCNs learning and transition into ICU and the range of individuals that can influence their success.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".