MétaCan
Menu
Back to cohort
Record W1994013204 · doi:10.2182/cjot.07.010

Cognitive Assessments for Older Adults: Which Ones are Used by Canadian Therapists and Why

2007· article· en· W1994013204 on OpenAlexafffundvenueabout
Alison Douglas, Lili Liu, Sharon Warren, Tammy Hopper

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2007
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsHamilton Health SciencesMcMaster UniversityPopulation Health Research Institute
FundersAlberta Association on GerontologyCanadian Occupational Therapy Foundation
KeywordsCognitionOccupational therapyMedicineCognitive Assessment SystemSample (material)PsychologyClinical psychologyCognitive impairmentPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Occupational therapists routinely evaluate cognition in older adults, yet little is known about which assessments they use and for what purposes. PURPOSE: To examine the standardised and non-standardised assessments used by occupational therapists to evaluate cognition. METHOD: A random sample of 1042 Canadian occupational therapists completed the questionnaire by e-mail, post, or Internet website (n=247, response rate: 24.5%). RESULTS: Respondents reported using 75 standardised and non-standardised measures. The assessments were grouped according to theoretical approach: bottom-up (assessment of cognitive components), top-down (assessment of function) and combined (either of above, plus interview). Theoretical approaches were used similarly across regions, despite differences in reporting of particular assessments. Therapists used more bottom-up assessments that were standardised, identified deficits, and easy to administer. They used more top-down assessments that were non-standardised, predicted function, and fit with their theoretical approach. CONCLUSION: It is recommended that standardised top-down assessments be developed to support evidence-based occupational therapy.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.231
GPT teacher head0.526
Teacher spread0.296 · 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

Citations39
Published2007
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Journal of Occupational TherapySame topicOccupational Therapy Practice and ResearchFrench-language works237,207