<i>Food Experiences and Eating Patterns of</i> Visually Impaired and Blind People
Bibliographic record
Abstract
PURPOSE: The number of visually impaired and blind Canadians will rise dramatically as our population ages, and yet little is known about the impact of blindness on the experience of food and eating. In this qualitative study, the food experiences and eating patterns of visually impaired and blind people were examined. Influencing factors were also explored. METHODS: In 2000, nine blind or severely visually impaired subjects were recruited through blindness-related organizations in British Columbia. Participants completed individual semi-structured, in-depth interviews. These were transcribed verbatim, coded, and analyzed to explicate participants' experiences. RESULTS: Participants experienced blindness-related obstacles when shopping for food, preparing food, and eating in restaurants. Inaccessible materials and environments left participants with a diet lacking in variety and limited access to physical activity. Seven participants were overweight or obese, a finding that may be related to limited physical activity and higher-than-average restaurant use. CONCLUSIONS: This is the first study in which the experience of food and eating is described from the perspective of visually impaired Canadians. Nutrition and blindness professionals must work together to reduce the food-related obstacles faced by visually impaired and blind people. Professionals must address both individual skill development and social and structural inequities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".