Environmental barriers and housing accessibility problems for people with Parkinson’s disease: A three-year perspective
Bibliographic record
Abstract
Background Although housing accessibility is associated with important health outcomes in other populations, few studies have addressed this in a Parkinson’s disease population.Aim To determine the most severe environmental barriers in terms of housing accessibility problems and how these evolved over 3 years among people with Parkinson’s disease.Material and Methods 138 participants were included (men = 67%; mean age = 68 years). The most severe environmental barrier were identified by the Housing Enabler instrument and ranked in descending order. The paired t-test was used to analyse changes in accessibility problems over time.Results The top 10 barriers remained largely unchanged over 3 years, but with notable changes in order and magnitude. ‘No grab bar in hygiene area’ and ‘Stairs only route’ were top-ranked in generating accessibility problems at baseline but decreased significantly (p = 0.041; p = 0.002) at follow-up. ‘Difficulties to reach refuse bin’ was top-ranked at follow-up, with a significant increase (p < 0.001) of related accessibility problems.Conclusions and Significance The new knowledge about how accessibility problems evolve over time could be used by occupational therapists to recommend more effective housing adaptations taking the progressive nature of Parkinson’s disease into account. On societal level, the results could be used to address accessibility problems systematically.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".