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Record W3012337817 · doi:10.1177/1471301220911304

Influence of perspectives on user adoption of wander-management strategies

2020· article· en· W3012337817 on OpenAlexafffundabout
Noelannah Neubauer, Lili Liu

Bibliographic record

VenueDementia · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Waterloo
FundersAGE-WELL
KeywordsDementiaSAFERStakeholderPhonePsychologyStigma (botany)Public relationsBusinessInternet privacyApplied psychologyMedicineComputer securityPsychiatryPolitical scienceDisease

Abstract

fetched live from OpenAlex

Sixty-percent of Canadians with dementia will wander and become lost. Strategies, such as wall murals that camouflage doors, and locator devices, offer proactive options for keeping persons with dementia who wander safer. Information that describes available strategies to mitigate this issue is diverse and inconsistent, creating challenges for caregivers and persons living with dementia when choosing helpful strategies. This project aimed to describe the spectrum of risks and risk mitigation strategies associated with dementia-related wandering. Thirty-eight phone interviews from across Canada were conducted with stakeholders including persons with dementia, paid and family caregivers, health professionals, law enforcement, and Alzheimer societies. Interviewees were asked about strategies that they have used to manage dementia-related wandering, and how their perceptions of risk, culture, stigma and geographical location may influence strategy adoption. Overall, a wide range of high- and low-tech solutions were used or suggested by participants, and factors such as risk, culture, geography and stigma were considered essential elements to successful adoption of these strategies. Results from this study highlight the need for unique combinations of strategies based on the type of stakeholder and influencing factors involved.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.019
GPT teacher head0.304
Teacher spread0.285 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations16
Published2020
Admission routes3
Has abstractyes

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