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Record W2152396819 · doi:10.5014/ajot.64.2.316

Occupational Therapists’ Capacity-Building Needs Related to Older Driver Screening, Assessment, and Intervention: A Canadawide Survey

2010· article· en· W2152396819 on OpenAlexafffund
Nicol Korner‐Bitensky, Anita Menon, Claudia von Zweck

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2010
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsUniversity of TorontoCanadian Association of Occupational TherapistsMcGill UniversityCentre for Interdisciplinary Research in Rehabilitation
FundersCanadian Institutes of Health Research
KeywordsRetrainingCompetence (human resources)Occupational therapyIntervention (counseling)MedicineContinuing educationOccupational safety and healthNeeds assessmentNursingMedical educationPsychologyPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Older driver safety is a growing concern. We identified capacity-building needs of occupational therapists related to older driver screening, assessment, and intervention. METHOD: A Canadawide survey was undertaken involving 133 occupational therapists working with an older clientele. A standardized questionnaire elicited information regarding (1) actual practices related to older driver screening, assessment, and intervention; (2) perceived competence; and (3) need for continuing education. RESULTS: Occupational therapists were twice as likely to use screening tools rather than in-depth assessments (n = 79 vs. n = 37). Only 25 occupational therapists offered on-road assessment, and even fewer offered retraining (n = 11). Occupational therapists more often felt very competent in domains related to screening as opposed to assessment, and most were interested in continuing education. CONCLUSION: Driving services offered were primarily related to screening compared with assessment or intervention. Occupational therapists would benefit from driving-related professional training aimed at enhancing professional capacity in this arena.

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)0.001
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 score0.018
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.089
GPT teacher head0.464
Teacher spread0.375 · 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

Citations39
Published2010
Admission routes2
Has abstractyes

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