Using Participant Observation to Enable Critical Understandings of Disability in Later Life: An Illustration Conducted With Older Adults With Low Vision
Bibliographic record
Abstract
Research with older adults aging with vision loss has typically been informed by a biomedical theoretical framework. With a growing focus, however, on critical disability perspectives, which locates disability within the environment, new methods of data collection, such as participant observation, are needed. This article, which reports on the findings from a critical ethnographic study conducted with older adults with age-related vision loss (ARVL), aims to share those insights gained through participant observation and to demonstrate the utility of this method. Three insights were gained including the adaptive strategies tacitly employed to navigate the physical environment, a grounded understanding of social interactions that transpire in everyday contexts, and negating the presence of older adults with ARVL when accompanied by a perceived caregiver. The study findings unpack how participant observation can be used to understand social constructions of disability and gain a holistic understanding of environmental influences on the disability experience of older adults with ARVL.
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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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".