Nursing interventions for patients with COVID-19: A medical record review and nursing interventions classification study
Bibliographic record
Abstract
PURPOSE: To describe the nursing interventions provided to patients with COVID-19 using the Nursing Interventions Classification. METHOD: This is a retrospective study involving the review of 1,344 patient records of adults admitted to a specialty hospital for COVID-19 in Tabriz, Iran. The nursing intervention was used to classify documented nursing care and interventions provided to COVID-19-positive patients from February 20 to August 20, 2020. Data were analyzed descriptively using SPSS16. FINDINGS: The 10 most frequently documented nursing interventions across in-patient (ward) and intensive care unit (ICU) contexts included Admission Care (7310), Environmental Management (6486), Health Education (5510), Infection Protection (6550), Medication Administration (2300), Positioning (0840), Respiratory Monitoring (3350), Vital Signs Monitoring (6680), Nausea Management (1450), and Diarrhea Management (0460). No records of distraction, relaxation techniques, or massage for anxiety reduction were documented. CONCLUSION: This study used a common language to describe nursing interventions for patients with COVID-19 admitted to a tertiary hospital. IMPLICATIONS FOR NURSING PRACTICE: The most commonly identified nursing interventions for COVID-19 identified in this study provide evidence-based insight into nurses' scope of practice in the COVID-19 in-patient context.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".