The impact of age stigma on recommendations of technology for older adults in rehabilitation settings: a secondary analysis of qualitative data
Bibliographic record
Abstract
Objective: Negative stereotypes about aging in healthcare settings can undermine the quality of care provided to older individuals. The goal of this study was to identify potential stigmatizing beliefs held by occupational therapists concerning the adoption of technology by older adults who require rehabilitation services. Materials and Methods: We utilized the stigma classification proposed by Link and Phelan (2001) as the basis for a thematic analysis of secondary data derived from two studies that examined rehabilitation healthcare professionals’ perceptions of technology. Data for the original research was collected through 15 interviews and four focus groups.Results: Age-related stigma was expressed in two ways: (1) the belief that older adults are unable to use technology effectively in later life (individual-level stigma), and (2) The recognition that funding is not prioritized for technological aids that address issues arising from the natural aging process (structural-level stigma).Discussion and Conclusions This exploratory study provides preliminary insights into how age-related stigma impacts the adoption of technology-based interventions in rehabilitation practices. Our goal is to raise awareness about how our assumptions regarding aging can influence the quality of care provided to older adults.
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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 | 0.037 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".