Construct validity of the Braden scale in acute- and long-term care settings in Austria: A structural equation modeling analysis
Bibliographic record
Abstract
Objective: The Braden scale is frequently used to assess pressure ulcer risk in health care settings. Selected psychometric properties have been tested using various methods of classical test theory in international studies. However, limited information on construct validity is available. Aim was to determine if the Braden subscale items correlate with the construct pressure ulcer risk and whether the construct validity concerning the factor structure of the Braden scale is adequate in acute and long-term settings.Methods: A quantitative design with secondary analysis of data from one acute (n = 328) and eight long-term care facilities (n = 311) in Austria was used to test construct validity. Data analysis included principal axis factor analysis with Promax rotation and assessment of internal consistency, followed by structural equation modeling.Results: For the acute care setting, a structure equation model with two latent factors and for the long-term care setting with one latent factor was tested according to principal axis factoring results. The Braden subscale items correlated with the construct pressure ulcer risk. Almost all examined model fit indices were within recommended reference values. Thus, the construct validity of the Braden scale was adequate in both settings.Conclusions: The factor structure in the acute care setting did not match that in the investigated long-term care setting. Further research regarding the construct validity of the Braden scale is therefore necessary.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".