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Record W3196552959 · doi:10.1177/14713012211051885

Working towards inclusion: Creating technology for and with people living with mild cognitive impairment or dementia who are employed

2021· article· en· W3196552959 on OpenAlexaff
Karan Shastri, Jennifer Boger, Sheida Marashi, Arlene Astell, Erica Dove, Ann‐Charlotte Nedlund, Anna Mäki‐Petäjä‐Leinonen, Louise Nygård

Bibliographic record

VenueDementia · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsResearch Institute for AgingUniversity of TorontoUniversity of Waterloo
FundersHorizon 2020 Framework ProgrammeJoint Programming Initiative More Years, Better Lives
KeywordsDementiaWorkforceInclusion (mineral)PsychologyParticipatory designCitizen journalismCognitive impairmentCognitionGerontologyPsychiatryMedicineSocial psychologyEngineeringPolitical science

Abstract

fetched live from OpenAlex

Earlier diagnosis and longer working careers is resulting in more individuals being identified as having Mild Cognitive Impairment or Early Onset Dementia (MCI/EOD) when they are still in the workforce. While there is growing interest in the dementia research community and beyond to develop technologies to support people with dementia, the use of technology for and by people with MCI/EOD in the workplace has had very little attention. This paper presents a two-part study involving interviews and participatory sessions to begin to understand the workplace experiences and the role of technology among people living with MCI/EOD. We present our findings from working with seven people with MCI/EOD and two care partners to explore technology design. Our results indicate several similarities as well as a few differences between MCI/EOD and later-onset dementia with respect to challenges using technology and design considerations for supporting engagement and use of technology. Lessons learned through the process of working with people with MCI/EOD through participatory methods is presented along with recommendations to foster an inclusive, respectful, and empowering experience for participants with MCI/EOD.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.283
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 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

Citations18
Published2021
Admission routes1
Has abstractyes

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