Physical Assessment Skills Used by Registered Nurses
Bibliographic record
Abstract
Background: Nurses work in a wide variety of settings. Therefore, it is essential for nursing educators to continually evaluate and adapt nursing programs to ensure that the curricula prepare students to practice in a variety of health care settings. In this study, we sought to determine which skills were routinely used by registered nurses and to examine whether the cumulative number of practice settings influenced the number, and type, of physical assessments used. Methods: An electronic or a paper survey was distributed to registered nurses who worked as nursing clinical instructors at a mid-sized university in this cross-sectional study. Measures of frequency and central tendency described participant characteristics and physical assessment skills. A Kruskal-Wallis test was used to investigate an association between the number of practice settings and physical assessment skills used. Results: Forty-nine surveys were completed (59.8% return rate), with 47 surveys used for inferential statistics. Practice settings ranged from one to six, with a median of two employment areas. Medical-surgical was the most common setting (65.3%). Respondents identified 25 physical assessment skills as being performed routinely in their clinical practice; 11 skills were routinely performed by 80% of respondents. The median number of core skills routinely performed in clinical practice was not statistically significant (x2 = 4.03; p = .25). Conclusion: Academic and clinical nursing educators, policymakers, and nursing managers can use this study’s findings to supplement decision-making concerning nursing employment and continuing education opportunities.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".