Understanding Risk in Daily Life of Diverse Persons with Physical and Sensory Impairments
Bibliographic record
Abstract
Managing risk of injury in daily life is a task common to all humans. However, people with impairments face significantly greater challenges in both assessing and managing risk of injury. To find out more about how individuals with impairments understand risk, we developed a qualitative study design based on semi-structured interviews. Seven people with a broad range of impairments were recruited for the study. The interviews were analyzed and organized into a codification tree subdivided into four main sections: safety and risk management, risk situation portrayal, perceptions of safety measures and finally loss of control and strong sensations. The study revealed that the difficulties related to managing risk in day-to-day situations are much higher than for people without impairments and, indeed, are possibly under reported in the literature. The realization that risk is ever present in the daily lives of people with impairments has led us to reconsider how we move forward on the remainder of our study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".