Relationship between elderly stroke patient caregivers scale and nursing diagnoses
Bibliographic record
Abstract
OBJECTIVE: To describe relationships between the ECPICID-AVC scale factors and the NANDA-I domains, classes, and Nursing Diagnoses (NDs). METHOD: Cross-mapping study between the NANDA-I taxonomy and ECPICID-AVC scale was constructed based on the eight ECPICID-AVC scale factors and the 13 NANDA-I domains. A descriptive analysis was performed to present the mapped elements. RESULTS: Areas of similarity and intersection were found between the eight ECPICID-AVC factors and nine NANDA-I domains, 19 classes, and 72 NDs. All scale factors were mapped with the Domain 1/Health Promotion, Class 2/Health Management and the ND "Frail elderly syndrome". FINAL CONSIDERATIONS: The ECPICID-AVC scale factors were mapped with nine domains, their classes and diagnoses. This study demonstrates the importance of identifying nursing diagnoses and their relationship with factors that evaluate caregiving capacity. The ECPICID-AVC can help nurses generate nursing diagnoses regarding the caregiver's needs and their capacities related to care to focus such needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".