Services, systems, and policies affecting community mobility for people with mobility impairments in Northern Iceland: An occupational perspective
Bibliographic record
Abstract
Background: Services, systems, and policies can affect what people do, including community mobility (CM), or the act of moving around within the community. People with mobility impairments meet various challenges to CM as the environment does not always accommodate their needs.Aim: To explore, through an occupational lens, how services, systems, and policies can restrict or support CM for people with mobility impairments.Methods: As the first phase of an exploratory case study, focus group interviews were conducted with two different groups: users of mobility devices, living in the town of Akureyri, Iceland, and people who have experience of providing or planning services for disabled people in the same area.Results: Five themes, “Being mobile: A key to meaningful occupations”, “Users as agents in their own lives”, “Means of transportation”, “Accessibility awareness”, and “Integration of services and systems”, identify important aspects that need to be addressed to better support CM.Conclusion: The findings suggest the need to further explore transportation service, personal assistance, and infrastructure services affecting accessibility; alongside the importance of incorporating occupational justice and rights values into policy implementation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".