Self-efficacy in Clinical Nurse Specialists During the COVID-19 Pandemic
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine the perceived self-efficacy of the clinical nurse specialist working in the United States during the COVID-19 pandemic and explore whether there was any difference in self-efficacy based on practice focus (spheres of impact) and if differences existed between self-efficacy and demographics. DESIGN: This study used a nonexperimental, correlational, cross-sectional design utilizing a voluntary, anonymous, 1-time survey administered through Qualtrics (Qualtrics, Provo, UT). METHODS: The National Association of Clinical Nurse Specialists and 9 state affiliates distributed the electronic survey starting late October 2021 through January 2022. Survey content consisted of demographics and the General Self-efficacy Scale, which measures the individual's perceived ability to cope and execute tasks when faced with hardship or adversity. Sample size was 105. RESULTS: Results included a high perception of self-efficacy of the clinical nurse specialist working during the pandemic, no statistical significance in practice focus, and a statistically significant difference in the scores of self-efficacy for participants with previous infectious disease experience compared with those without experience. CONCLUSIONS: Clinical nurse specialists with previous infectious disease experience can guide policy, be utilized in multifaceted roles to support future infectious disease outbreaks, and develop training to prepare and support clinicians during crises such as pandemics.
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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 | 0.003 | 0.013 |
| 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".