Validity of the Diabetic Wound Assessment Learning Tool
Bibliographic record
Abstract
PURPOSE: The development of the Diabetic Wound Assessment Learning Tool (DiWALT) has previously been described. However, an examination of its application to a larger, more heterogeneous group of participants is lacking. In order to allow for a more robust assessment of the psychometric properties of the DiWALT, we applied it to a broader group of participants. MATERIALS AND METHODS: We built validity evidence for the tool by assessing 74 clinician participants' during two simulated wound care scenarios: Two assessors independently rated each participant using our tool, with a total of five raters providing scores. We evaluated validity evidence using generalizability theory analyses and by comparing performance scores across the three experience levels using ANOVA. RESULTS: The tool differentiated between novices and the other two groups well (p < 0.01) but not between intermediates and experts (p = 0.34). Our generalizability coefficient was 0.87, and our phi coefficient was 0.87. CONCLUSION: The accumulated validity evidence suggests our tool can be used to assess novice clinicians' competence in initial diabetic wound management during simulated cases. Further work is required to clarify the DiWALT's performance in a broader universe of generalisation and to examine evidence for its extrapolation and implications inferences.
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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 | 0.050 | 0.177 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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".