Nursing intervention assessment tool fall prevention in elderly people with systemic arterial hypertension
Bibliographic record
Abstract
Background and objective: Elderly people are at greater risk for falls and, therefore, need effective interventions to prevent them. The aim of the study was to develop an assessment tool for nursing intervention fall prevention to elderly with arterial hypertension and with nursing diagnosis Risk of falls.Methods: A methodological study, accomplished in four stages: activities selection of the fall Prevention intervention from Nursing Interventions Classification (NIC); 2) construction of constructive definitions and operational for selected activities; 3) expert validation of constructed definitions; 4) pretest of the final assessment tool.Results: The experts selected 50 activities out of 65 presented by NIC. The constitutive and operational definitions of the 50 activities were elaborated. From the focus group, some activities were grouped and the content of others changed. The pretest showed that, although the application of the assessment tool with the definitions take longer, it was more complete and targeted. The final assessment tool contains 28 activities with constitutive definitions and operational.Conclusions: The produced assessment tool has nursing activities with constitutive and operational definitions suitable for clinical nursing practice. It is believed that it can lead the intervention of the nurses in preventing falls in elderly people with SAH and with the nursing diagnosis Risk of falls.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".