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Record W3135897266 · doi:10.1093/geront/gnab026

Moving Toward the Promise of Participatory Engagement of Older Adults in Gerotechnology

2021· article· en· W3135897266 on OpenAlexafffund
Alisa Grigorovich, Pia Kontos, Amanda J. Jenkins, Susan Kirkland

Bibliographic record

VenueThe Gerontologist · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsToronto Rehabilitation InstituteUniversity Health NetworkDalhousie University
FundersCanadian Institutes of Health Research
KeywordsParticipatory action researchCitizen journalismScope (computer science)Participatory designAction (physics)Field (mathematics)Key (lock)Participatory GISSociologyPublic relationsEngineering ethicsPsychologyPolitical scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

Limited uptake and use of developed technologies by older adults have prompted interest in participatory design and related approaches in the gerotechnology field. Despite this, recent systematic reviews suggest that researchers continue to passively engage older adults in research projects, often only providing advice or feedback in the early or later phases of research. A key barrier to more meaningful and active engagement of older adults is a lack of understanding as to how participatory design differs from other participatory approaches, and in particular, participatory action research. We address this gap in understanding by exploring the theoretical similarities and differences of participatory design and participatory action research, including their scope, goals, and the nature of the involvement of older adults in each. We conclude with key barriers that are critical to address in order to achieve greater involvement of older adults in gerotechnology and to broaden and enrich the goals of this field.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.231
metaresearch head score (Gemma)0.192
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.231
Threshold uncertainty score0.948

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2310.192
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.002
Science and technology studies0.0070.014
Scholarly communication0.0150.022
Open science0.0040.026
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.067
GPT teacher head0.332
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations39
Published2021
Admission routes2
Has abstractyes

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