Construction and validation of a scale of assessment of self‐care behaviors with arteriovenous fistula in hemodialysis
Bibliographic record
Abstract
Several guidelines recommend the importance of educating the patient about the care of vascular access. Nurses have a key role in promoting the development of self-care behaviors by providing the necessary knowledge to patients, so that they develop the necessary skills to take care of the arteriovenous fistula (AVF). This article describes the process of building a scale of assessment of self-care behaviors with arteriovenous fistula in hemodialysis (ASBHD-AVF). This is a cross-sectional study in which the development, construction, and validation process followed the directions of the authors Streiner and Norman. This is a convenience sample, sequential, and nonprobabilistic constituted by 218 patients. The study was conducted in two stages during 2012-2014. The first phase corresponds to the scale construction process, 64 patients participated, while the second corresponds to the evaluation of metric properties and 154 patients participated. The principal component analysis revealed a two-factor structure, with factorial weights between 0.805 and 0.511 and between 0.700 and 0.369, respectively, explaining 39.12% of the total variance of the responses. The Cronbach's alpha of the subscale management of signs and symptoms is 0.797 and from the subscale prevention of complications is 0.722. The ASBHD-AVF revealed properties that allow its use to assess the self-care behaviors in the maintenance and conservation of the AVF.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".