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Record W2776858115 · doi:10.5770/cgj.20.264

Driving and Dementia: Workshop Module on Communicating Cessation to Drive

2017· article· en· W2776858115 on OpenAlexafffundvenueabout
Anna Byszewski, Barbara Power, Linda Lee, Glara Gaeun Rhee, Robert Parson, Frank Molnar

Bibliographic record

VenueCanadian Geriatrics Journal · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsMcMaster UniversityCentre for Family MedicineOttawa HospitalUniversity of Ottawa
FundersOttawa Hospital Research InstituteUniversity of Ottawa
KeywordsMedicineDementiaCognitionMEDLINEHealth professionalsInformation exchangeApplied psychologyHealth carePsychiatryPsychologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: For persons with dementia (PWD), driving becomes very dangerous. Physicians in Canada are legally responsible to report unfit drivers and then must disclose that decision to their patients. That difficult discussion is fraught with challenges: physicians want to maintain a healthy relationship; patients often lack insight into their cognitive loss and have very strong emotional reactions to the loss of their driving privileges. All of which may stifle the exchange of accurate information. The goal of this project was to develop a multimedia module that would provide strategies and support for health professionals having these difficult conversations. METHODS: Literature search was conducted of Embase and OVID MedLine on available driving and dementia tools, and on websites of online tools for communication strategies on driving cessation. A workshop module was developed with background material, communication strategies, links to resources and two videos demonstrating the "bad" then the "good" ways of managing this emotionally charged discussion. RESULTS: < .001), and comfort and willingness in discussing the subject improved. CONCLUSION: This project demonstrated the positive impact of the module on improving health professionals' attitude and readiness to communicate driving cessation to PWD.

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)0.002
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 score0.380
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0100.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.376
Teacher spread0.316 · 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

Citations8
Published2017
Admission routes4
Has abstractyes

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