A translation and preliminary validation of the Dutch Wound-QoL questionnaire
Bibliographic record
Abstract
Abstract Background Chronic wounds have a major impact on patients’ health-related quality of life (HRQoL). Therefore, measuring HRQoL is an indispensable part of the treatment of patients with chronic wounds.Objectives To translate and validate the Wound-QoL, a wound-specific HRQoL questionnaire, in a Dutch population.Methods The Wound-QoL was translated into Dutch according to the international standards. Patients with chronic wounds were asked to complete questionnaires at baseline (T0) and after 6 weeks (T1), including Wound-QoL, EQ-5D-3L (a generic questionnaire to measure HRQoL) and a visual analogue scale (VAS) measuring wound pain. If patients were not able to complete the questionnaire by themselves, it was read out to them by a nurse. Further data were obtained from medical records.Results Of the 120 patients included, 64 (53.3%) completed the questionnaire by themselves. To 55 patients (45.8%), the questionnaire was read out. The internal consistency of the Wound-QoL global score was high at both time points (T0: Cronbach’s α = 0.89, T1: Cronbach’s α = 0.92). The item selectivity for global score ranged from r = 0.25 to r = 0.77 at T0 and from r = 0.40 to r = 0.79 at T1. Overall, the self-completion and read-out subgroups showed similar internal consistency and item selectivity scores. With regard to convergent validity, significant correlations were found between Wound-QoL and EQ-5D-3L (T0: r=-0.45, p < 0.001, T1: r=-0.50, p < 0.001) as well as between Wound-QoL and pain VAS (T0: r = 0.23, p = 0.012, T1: r = 0.37, p = 0.001) at both time points. Responsiveness analyses showed significant correlations between changes in Wound-QoL and changes in EQ-5D-3L (r=-0.37, p < 0.001), pain VAS (r = 0.24, p = 0.044) and wound size (r = 0.24, p = 0.013). The self-completion and read-out subgroups showed differences in convergent validity and responsiveness.Conclusions The results indicate that the Dutch version of the Wound-QoL has positive psychometric properties. However, more research is needed to further explore the differences between self-completed and read-out questionnaires.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".