(Re) evaluating Critical Care Nurse Support Program(s) in a Tertiary Care Hospital: Intersecting the Art and Science of Nursing
Bibliographic record
Abstract
There is a growing critical care nurse staffing shortage with increases in nurse vacancy rates. Moral distress has been exacerbated by the SARS-CoV-2 (COVID-19) pandemic and, in particular, impacting critical care nurses. COVID-19 is a significant contributor to staffing shortages and continued nursing crisis. Thus, the impetus for the Problem of Practice (PoP): the lack of support to address the psychological, emotional, and spiritual distress suffered by critical care registered nurses in a tertiary care hospital in Central Ontario. To comprehend the realities of working in the intensive care units, leaders must first understand nurses’ lived experiences, narratives, and what it means to work on the frontline in an intensive care unit. The Organizational Improvement Plan (OIP) is underpinned by interpretive phenomenology and authentic and transformational leadership approaches. Lewin’s three-stage force field model of change theory is utilized for leading change and Burke and Litwin’s performance change model for the organizational analysis. The overall goal of the OIP is to implement a change plan that brings leaders and critical care registered nurses together to co-create support program(s) to address critical care nurses’ psychological, emotional, and spiritual distress, decrease nurse attrition, and enhance critical care nurses’ well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".