Identifying health-related quality of life concepts to inform the development of the WOUND-Q
Bibliographic record
Abstract
OBJECTIVE: The impact of hard-to-heal wounds extends beyond traditional clinical metrics, negatively affecting a patient's health-related quality of life (HRQoL). Yet treatment outcomes are seldom measured from the patient's perspective. The purpose of the present study was to perform in-depth qualitative interviews with patients diagnosed with varying types of hard-to-heal wounds to identify outcomes important to them. METHOD: Participants were recruited from wound care clinics in Canada, Denmark, the Netherlands and the US, and were included if they had a hard-to-heal wound (i.e., lasting ≥3 months), were aged ≥18 years, and fluent in English, Dutch or Danish. Qualitative interviews took place between January 2016 and March 2017. An interpretive description qualitative approach guided the data analysis. Interviews were audio-recorded, transcribed and coded line-by-line. Codes were categorised into top-level domains and themes that formed the final conceptual framework. RESULTS: We performed 60 in-depth interviews with patients with a range of wound types in different anatomic locations that had lasted from three months to 25 years. Participants described outcomes that related to three top-level domains and 13 major themes: wound (characteristics, healing); HRQoL (physical, psychological, social); and treatment (cleaning, compression stocking, debridement, dressing, hyperbaric oxygen, medication, suction device, surgery). CONCLUSION: The conceptual framework developed as part of this study represents the outcome domains that mattered the most to the patients with hard-to-heal wounds. Interview quotes were used to generate items that formed the WOUND-Q scales, a patient-reported outcome measure for patients with hard-to-heal wounds.
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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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".