Comparing the capacity of nurses and nursing students in assessing patient problems during clinical internship: A descriptive comparative study
Bibliographic record
Abstract
Objective: No studies were found in the literature which compared the capacity of nurses and nursing students to assess patient problems using the clinical cases followed during internship. Therefore, the aim of this study was to formulate a method for comparing these skills in cases followed during a practical clinical internship.Methods: The sample studied was made up of students of the degree course in nursing during their internship and by community nurses, both trained in using assessment. Each student identified a patient and carried out an assessment of the problems according to the functional patterns of M. Gordon; the nurses also simultaneously carried out the same activity without comparing their work with that of the students. A method was formulated for evaluating the correctness of the two evaluations.Results: The results relative to the assessment showed a percentage of correctness of 85.77% for the students and 91.28% for the nurses with a statistically significant difference (p = .027).Conclusions: The results obtained demonstrated that the students in the last year of their degree course in nursing had developed a good capacity of assessment during their internship in clinical practice in the community in line with the capacity of the nurses who taught them.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".