Validation of an instrument to assess fluid control in outpatient hemodialysis patients
Bibliographic record
Abstract
Purpose: To validate an instrument to assess fluid control in outpatient hemodialysis patients, using the NANDA International (NANDA-I), Nursing Interventions Classification (NIC), and Nursing Outcomes Classification (NOC) terminologies.Methods: A methodological study was carried out in two steps: (1) construction of an instrument composed of operational definitions of the defining characteristics of the nursing diagnosis Excess fluid volume, indicators of the NOC outcome Fluid balance, and activities of the NIC intervention Fluid management, from a narrative literature review, and (2) content validation of the instrument by five experts through a focus group.Results: The instrument was composed of operational definitions of 27 defining characteristics of Excess fluid volume, 23 Fluid balance indicators, and 13 Fluid management activities. Twenty-five out of the 27 defining characteristics were considered valid. Two defining characteristics were excluded from the instrument, as they were considered unsuitable for assessing outpatients on hemodialysis. Thirteen out of the 23 indicators of Fluid balance were reformulated, and two were removed. Thirteen activities of Fluid management were reformulated.Conclusions: An instrument was built incorporating components of the nursing diagnosis Excess fluid volume, the nursing outcome Fluid balance, and the nursing intervention Fluid management. The instrument was considered valid in terms of content and can be used to assess outpatients on hemodialysis. Implications for nursing practice: The instrument created may contribute to the standardization, qualification, and improvement of nursing practice in outpatient hemodialysis facilities.
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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 | 0.065 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".