Fashion industry perceptions of clothing design for persons with a physical disability: the need for building partnerships for future innovation
Bibliographic record
Abstract
Introduction: Persons with a physical disability may need adapted clothing to facilitate their full participation in society; it is unclear what information designers use to create adapted clothing. Objective: Explore the perspectives of fashion industry representatives regarding adapted clothing and gauge their receptiveness towards academic inquiry. Methods: Semi-structured interviews with five female adapted clothing designers were conducted, transcribed verbatim, coded, and analyzed thematically. Results: Participants felt research (i.e. knowledge and guidance) could benefit the design process and spoke about industry barriers (e.g. time, manufacturing, human and material resources, marketing, level of importance) to designing adapted clothing. Conclusions: Strengthening collaborations with stakeholders (e.g. researchers, designers, consumers, health professionals, caregivers) may add credibility to future adapted clothing designs and bridge the gap between research and practice. Engagement from fashion design trainees could also contribute to growing a more socially responsible industry.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".