Ageism in the Discourse and Practice of Designing Digital Technology for Older Persons: A Scoping Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Involving older persons in the design process of digital technology (DT) promotes the development of technologies that are appealing, beneficial, and used. However, negative discourse on aging and ageism are potential underlying factors that could influence which and how DTs are designed and how older persons are involved in the design process. This scoping review investigates the explicit and implicit manifestations of ageism in the design process of DT. RESEARCH DESIGN AND METHODS: Seven databases were screened for studies reporting on the design of DT with older persons between January 2015 and January 2020. Data regarding study and DT characteristics, discourse about older persons, and their involvement in the design process were extracted, coded, and analyzed using critical discourse analysis. RESULTS: Sixty articles met the inclusion criteria and were included in the analysis. Various forms of exclusion of older persons from the design process were identified, such as no or low involvement, upper-age limits, and sample biases toward relatively "active," healthy and "tech-savvy" older persons. Critical discourse analysis revealed the use of outdated language, stereotypical categorizations, and/or design decisions based on ageism in 71.7% of the studies. DISCUSSION AND IMPLICATIONS: A discrepancy was found between an "ideal" discourse regarding the involvement of older persons throughout the design process and actual practice. Manifestations of ageism, errors, and biases of designing DT with older persons are discussed. This article calls for more authentic inclusion of older persons and higher awareness toward the implications of ageism in the design process of DT.
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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 | 0.035 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".