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Record W3120917595 · doi:10.1017/s0714980820000409

‘We’re Not Doing It To Be Nasty’: Caregivers’ Ethical Dilemmas in Negotiating Driving Safety with Older Adults

2021· article· en· W3120917595 on OpenAlexaff
Michelle N. Lafrance, Elizabeth Dreise, Lynne Gouliquer, Carmen Poulin

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2021
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsUniversity of FrederictonUniversity of New BrunswickLaurentian UniversitySt. Thomas University
Fundersnot available
KeywordsThematic analysisNegotiationContext (archaeology)DementiaInterpersonal communicationDilemmaBlamePsychologyInterpersonal violenceGerontologyQualitative researchMedicineSocial psychologySuicide preventionDiseasePoison controlSociologyMedical emergency

Abstract

fetched live from OpenAlex

The purpose of this research was to investigate how informal caregivers of older adults cope with and negotiate driving safety when their loved one is no longer safe to drive. Fifteen informal caregivers of an older adult living at home took part in the present study. Participants cared for individuals with a range of health conditions that significantly impaired driving safety, including dementia, Parkinson's disease, macular degeneration, and stroke. A thematic analysis of participants' accounts identified the complex interpersonal, social, and organisational context they encountered when their loved one did not recognise or acknowledge limitations in their ability to drive. This analysis highlights the ethical dilemma at the heart of caregivers' experiences and identifies stake and blame as key considerations in the development of sensitive and effective policies and practices.

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)0.025
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.046
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0190.020
Scholarly communication0.0080.007
Open science0.0020.008
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.291
Teacher spread0.269 · 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.

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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicOlder Adults Driving StudiesFrench-language works237,207