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Record W2899011802 · doi:10.1097/tgr.0000000000000205

Examining Occupational Therapists' Awareness of Medical Fitness-to-Drive Legislation Using a Knowledge-to-Action Approach

2018· article· en· W2899011802 on OpenAlexaffabout
Ruheena Sangrar, Lauren E. Griffith, Lori Letts, Brenda Vrkljan

Bibliographic record

VenueTopics in Geriatric Rehabilitation · 2018
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLegislationAction (physics)Knowledge translationMedicineMedical educationPublic relationsKnowledge managementPolitical science

Abstract

fetched live from OpenAlex

One Canadian province now requires occupational therapists (OTs) to report medically at-risk drivers to the transportation authority. This study examined OTs' legal and professional responsibilities with regard to medical fitness to drive. Two knowledge translation models guided the study design. Semi-structured interviews were conducted with 7 OTs, a geriatrician, as well as representatives from professional regulatory organizations and the licensing bureau. Emergent themes highlight gaps in the translation of knowledge specific to professional responsibilities as well as ethical risks to client rapport. Further education on relevant policies is suggested and changes to existing resources that support clinical practice.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.061
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.165
GPT teacher head0.482
Teacher spread0.317 · 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

Citations6
Published2018
Admission routes2
Has abstractyes

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