Structural factors affecting community mobility for people with mobility impairments in Iceland: A human rights and occupational perspective
Bibliographic record
Abstract
Moving between places in the community, or community mobility, is both an occupation and a means to other occupations. In order to move around and participate in society, people with mobility impairments need modifications as they encounter multiple barriers in their everyday lives. This discussion paper makes recommendations for policy development, especially as it relates to accessibility and transportation services for disabled people. This work highlights aspects that need to be incorporated into policy making to support community mobility for people with mobility impairments: (1) Incorporation of occupational values; (2) Participatory policy making; (3) Consistency and transparency in policy documents; and (4) Comprehensive monitoring system. The recommendations are discussed and reflected upon in the light of human rights, occupational science, and the Capabilities Approach. This paper highlights issues that arise at the system level and how limitations of policies and their implementation can lead to multiple capability failures and violations of human rights – such as the difficulties people with mobility impairments face if society fails to provide the necessary means to support their community mobility – and the various ways that situation may affect their other chosen occupations.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".