Content validity of a home-based person-environment interaction assessment tool for visually impaired adults
Bibliographic record
Abstract
Home-based assessments require in-depth analyses of daily living difficulties. No assessment tool that has been validated with visually impaired adult subjects has allowed such analysis. This research adapted a home-based person-environment interaction assessment tool designed for persons who are visually impaired. The Model of Competence, an explanatory model of the person-environment relationship, served as the conceptual framework. A qualitative study was conducted with professionals, visually impaired persons, and informal caregivers. Focus groups and semistructured individual interviews were used for data collection. The content and form had to be modified to adapt the assessment tool for use with visually impaired adults. This qualitative study documents the content validity of the Home Assessment of Person-Environment Interaction-Visual Version. The assessment tool will provide vision rehabilitation professionals better screens and explanations of handicap-created situations faced by visually impaired persons at home. By using a structured analysis based on a person-environment theoretical model, this new assessment tool fills a scientific and clinical gap, optimizes the evaluation process, and documents the intervention plan by providing an understanding of the home context.
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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 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".