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Record W2907803746 · doi:10.1037/pag0000325

Exploring potential prejudice toward older adult mobility device users.

2018· article· en· W2907803746 on OpenAlexaff
Cara C. MacInnis, Carlina Ulrich, Candace Konnert

Bibliographic record

VenuePsychology and Aging · 2018
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychologyPrejudice (legal term)Developmental psychologySocial psychology

Abstract

fetched live from OpenAlex

Many older adults require assistive technology to maintain mobility (e.g., canes, walkers, wheelchairs, or scooters), but concerns about experiencing prejudice because of mobility devices can deter use. We explore this potential prejudice in a sample recruited through online crowdsourcing. Overall, prejudice toward older adult mobility device users was not observed. Older adult mobility device users were evaluated more positively than common prejudice target groups. However, heightened prejudice toward older adult mobility device users was observed among those higher in authoritarianism or social dominance orientation. This was explained by perceptions that older adult mobility device users are a greater threat to resources (e.g., health care spending, time, attention) among those higher in these qualities. This pattern was present at all ages assessed but was stronger for those who were younger versus older. Relationships between ideology and heightened threat from older adult mobility device users were not present for those older than 60 years of age. Our results demonstrate that concerns about this prejudice are not completely unwarranted. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.154
GPT teacher head0.416
Teacher spread0.261 · 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 teacher head, not a consensus.

Study designObservational
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

Citations2
Published2018
Admission routes1
Has abstractyes

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