Development and psychometric property testing of a skin tear knowledge assessment instrument (OASES) in 37 countries
Bibliographic record
Abstract
AIM: To develop and psychometrically evaluate a skin tear knowledge assessment instrument (OASES). DESIGN: Prospective psychometric instrument validation study. METHOD: The skin tear knowledge assessment instrument was developed based on a literature review and expert input (N = 19). Face and content validity were assessed in a two-round Delphi procedure by 10 international experts affiliated with the International Skin Tear Advisory Panel (ISTAP). The instrument was psychometrically tested in a convenience sample of 387 nurses in 37 countries (April-May 2020). Validity of the multiple-choice test items (item difficulty, discriminating index, quality of the response alternatives), construct validity, and test-retest reliability (stability) were analysed and evaluated in light of international reference standards. RESULTS: A 20-item instrument, covering six knowledge domains most relevant to skin tears, was designed. Content validity was established (CVI = 0.90-1.00). Item difficulty varied between 0.24 and 0.94 and the quality of the response alternatives between 0.01-0.52. The discriminating index was acceptable (0.19-0.77). Participants with a theoretically expected higher knowledge level had a significantly higher total score than participants with theoretically expected lower knowledge (p < .001). The 1-week test-retest intraclass correlation coefficient (ICC) was 0.83 (95% CI = 0.78-0.86) for the full instrument and varied between 0.72 (95% CI = 0.64-0.79) and 0.85 (95% CI = 0.81-0.89) for the domains. Cohen's Kappa coefficients of the individual items ranged between 0.21 and 0.74. CONCLUSION: The skin tear knowledge assessment instrument is supported by acceptable psychometric properties and can be applied in nursing education, research, and practice to assess knowledge of healthcare professionals about skin tears. IMPACT: Prevention and treatment of skin tears are a challenge for healthcare professionals. The provision of adequate care is based on profound and up-to-date knowledge. None of the existing instruments to assess skin tear knowledge is psychometrically tested, nor up-to-date. OASES can be used worldwide to identify education, practice, and research needs and priorities related to skin tears in clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".