OPEN JOURNAL SYSTEMS ARTICLE TOOLS Print this article Indexing metadata How to cite item Email this article Email the author Journal Help USER You are logged in as... sherrikeller My Profile Log Out LANGUAGE Select Language English JOURNAL CONTENT Search Search Scope All Browse By Issue By Author By Title FONT SIZE Make font size smallerMake font size defaultMake font size larger INFORMATION For Readers For Authors For Librarians CURRENT ISSUE Atom logo RSS2 logo RSS1 logo HOME ABOUT USER HOME SEARCH CURRENT ARCHIVES ANNOUNCEMENTS CANO/ACIO Home > Vol 29, No 2 (2019) > Savage Validation of the Malignant Wound Assessment Tool – Research (MWAT-R) using cognitive interviewing
Bibliographic record
Abstract
Malignant wounds as a result of cancer are under-recognized for the physical and emotional distress they cause patients and their families. Unfortunately, there is a lack of valid and reliable screening and assessment tools to aid in the management of malignant wounds. This study aims to validate a patient-reported outcome measurement tool, Malignant Wound Assessment Tool - Research (MWAT-R). Eight patients were recruited and interviewed using the cognitive interviewing methodology to validate this tool. Patients' understanding and overall impression of the MWAT-R were explored. Our findings showed that the wording and response options posed challenges for patients in completing the tool. Overall, participants felt that questions captured the key issues related to dealing with a malignant wound and accounted for the patients' perspective. Establishing the content and face validity of the MWAT-R from the patients' perspectives using cognitive interviews has provided further evidence to the validity of this tool.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 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".