Factors that contribute to a NANDA nursing diagnosis of risk for frail elderly syndrome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it