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Record W2737070939 · doi:10.1080/07380577.2017.1337282

Looking Forward and Looking Back: Older Adults' Views of the Impacts of Stopping Driving

2017· article· en· W2737070939 on OpenAlexafffundabout
Nadia Mullen, Barbara Parker, Elaine Wiersma, Arne Stinchcombe, Michel Bédard

Bibliographic record

VenueOccupational Therapy In Health Care · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsSt. Joseph's Care GroupLakehead Psychiatric HospitalLakehead University
FundersCanadian Institutes of Health ResearchNorthwestern University
KeywordsPsychological interventionPsychologyFocus groupResistance (ecology)Applied psychologyGerontologyMedicineBusinessPsychiatryMarketing

Abstract

fetched live from OpenAlex

This project aimed to identify the impact of driving cessation from the perspectives of older drivers and former drivers. Participants included 17 adults aged 65-88 years residing in a city in Northwestern Ontario, Canada. Using a semi-structured interview guide (with questions regarding mobility, personal impact, impact on others, engagement with life, and finances), two focus groups were held with nine current drivers, and one-on-one interviews were held with six former drivers and two current drivers. Two themes emerged concerning stopping driving. The first theme included discussions on experiencing lifestyle changes, relationship impacts, and emotional impacts. The second, the adjustment to stopping driving, included practical adaptations, and emotional responses such as appreciation, resistance, acceptance, and being positive. Although the impacts of stopping driving were substantial, there were few discrepancies between what was anticipated and what was experienced. This information could assist with developing interventions to ease the transition to former-driver status.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0040.003
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.439
Teacher spread0.379 · 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

Citations21
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueOccupational Therapy In Health CareSame topicOlder Adults Driving StudiesFrench-language works237,207