Factors that contribute to a NANDA nursing diagnosis of risk for frail elderly syndrome
Bibliographic record
Abstract
Abstract OBJECTIVE Identify the risk factors that contribute to a NANDA-I nursing diagnosis of risk for frail elderly system. METHOD Cross-sectional study with 395 elderly subjects, conducted from November 2010 to January 2013, in a university hospital in South of Brazil. Sociodemographic data were collected and levels of frailty were identified according to the Edmonton Frail Scale. RESULTS A total of 177 (44.81%) participants were classified as frail. There was a significant association between frailty and being female (p=0.031), nonwhite (p=0.008), having no romantic partner (p=0.014), no schooling (p=0.001), a monthly income lower than the minimum wage (p=0.034), and preexisting morbidities for respiratory diseases (p=0.003) as well as infectious and parasitic diseases (p=0.040). Diseases of the tracts genitourinary (p=0.035), respiratory (p=0.001) and blood (p=0.035) were the primary reasons for hospitalization. CONCLUSIÓN Los resultados contribuyen para el desarrollo e implementación del diagnóstico de enfermería en estudio en el ambiente hospitalario.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".