The Swedish version of the attitude towards pressure ulcer prevention instrument for use in an operating room context (APUP-OR): A nationwide psychometric evaluation
Bibliographic record
Abstract
INTRODUCTION: To assess operating room (OR) nurses' attitudes towards pressure ulcer prevention, the Attitude towards Pressure Ulcer Prevention (APuP) instrument was developed. AIM: The aim of this study was to psychometrically evaluate the Attitude towards Pressure Ulcer Prevention (APuP) instrument in a Swedish OR context. MATERIALS AND METHODS: A psychometric evaluation study was conducted, using a convenience sample, between February and August 2020. Validity (content, construct, discriminatory power) and reliability (stability and internal consistency) were evaluated. RESULTS: The first survey (test) was completed by 284 Swedish OR nurses, of whom n = 50 (17.6%) completed the second survey (retest). A Principal Component Analysis was conducted for the 13-item instrument. The KMO value for this model was 0.62. Bartlett's test for sphericity was statistically significant (p 0.001). Five factors were identified which accounted for 56% of the variance in responses related to attitudes toward pressure ulcer prevention. The Cronbach's α for the instrument "attitude towards Pressure Ulcer Prevention" was 0.66. The intraclass correlation coefficient was 0.49 (95% CI = 0.25-0.67). CONCLUSION: This Swedish version of the APuP- OR is the first step in the development of an instrument to measure OR nurses' attitudes towards PU prevention in a Swedish OR context. The reliability of the instrument was low and the validity moderate. A larger sample and the revision or addition of items related to the context of the operating room should be considered in order to confirm aspects of the psychometric properties.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".