Validity and reliability test of NIHSS-SDKI and CNS-SDKI in determining actual nursing diagnosis and severity of stroke patients
Bibliographic record
Abstract
Background: The National Institutes of Health Stroke Scale (NIHSS) and The Canadian Neurological Stroke Scale (CNS) are two assessment tools often used for the assessment of neurological status in stroke patients. However, there are no assessment tools that use the standard nursing diagnoses developed in Indonesia, namely the Standar Diagnosis Keperawatan Indonesia (SDKI), developed by the Indonesian National Nurses Association organization.Objective: This study aims to test the validity and reliability of the NIHSS and CNS combined with the SDKI so that they can be used to ensure the accuracy of the measuring instrument, determine its consistency, improve the quality of the assessment, and provide a basis for further research.Methods: This study is an observational study with a cross-sectional study design. The study was conducted at hospitals in Pontianak City, namely Tanjungpura University and Sultan Syarif Mohammad Alkadrie Hospital. The population in this study were all non-hemorrhagic stroke patients treated in the hospital, with a sample size of 30 patients. Sampling in this study was conducted using purposive sampling. Data collection began with the preparation of NIHSS and CNS instruments combined with SDKI, and then respondents' demographic data were collected. Data were analyzed using Pearson Correlation with SPSS statistical tools.Results: Based on the results of the validity and reliability tests, it was found that the NIHSS-SDKI and CNS-SDKI had a calculated r value> from the r table and Cronbach alpha value> 0.7 so that the NIHSS and CNS instruments were declared valid and reliable.Conclusion: Research on the NIHSS-SDKI and CNS-SDKI is valid and reliable, and it can be used as a tool to measure severity and help nurses make nursing diagnoses in accordance with the Indonesian Nursing Diagnosis Standards.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".