Quality of Nursing Diagnoses: Evaluation of an Educational Intervention
Bibliographic record
Abstract
PURPOSE: To investigate the effects on the quality of nursing diagnostic statements in patient records after education in the nursing process and implementation of new forms for recording. METHODS: Quasi-experimental design. Randomly selected patient records reviewed before and after intervention from one experimental unit (n = 70) and three control units (n = 70). A scale with 14 characteristics pertaining to nursing diagnoses was developed and used together with the instrument (CAT-CH-ING) for record review. FINDINGS: Quality of nursing diagnostic statements improved in the experimental unit, whereas no improvement was found in the control units. Serious flaws in the use of the etiology component were found. CONCLUSION. Nurses must be more concerned with the accuracy and quality of the nursing diagnoses and the etiology component needs to be given special attention. PRACTICE IMPLICATIONS: Education of RNs in nursing diagnostic statements and peer review using standardized evaluation instruments can be means to further enhance RNs' documentation practice.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".