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Record W4321783799 · doi:10.1080/02703181.2023.2180566

Perceived knowledge needs of occupational therapists for evaluating seniors with cognitive impairments

2023· article· en· W4321783799 on OpenAlexaff
Patricia Briskie-Semeniuk, Nathalie Bier, Mélanie Couture, Brigitte Vachon, Patrícia Belchior

Bibliographic record

VenuePhysical & Occupational Therapy In Geriatrics · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversité de SherbrookeUniversité de MontréalInstitut Universitaire en Santé Mentale de QuébecMcGill UniversityInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsOccupational therapyKnowledge translationThematic analysisPsychologyCognitionFocus groupMedical educationSalaryPopulationQualitative researchApplied psychologyGerontologyClinical psychologyMedicineKnowledge managementPsychiatry

Abstract

fetched live from OpenAlex

Aims This study describes the knowledge and tool needs identified by occupational therapists to improve their evaluation of older adults facing cognitive impairments and identify their preferred strategies for knowledge translation.Methods A descriptive qualitative study design was used. Four focus groups were conducted with 16 occupational therapists working with cognitively impaired older adults in institutional and community care settings. An inductive approach was used to analyze the thematic content of the interviews.Results Occupational therapists identified a need for up-to-date knowledge of cognitive systems and disorders and knowledge related to legal capacity and protection regimes. A need for short standardized performance-based tests was also identified. Preferred knowledge translation strategies included a combination of formal learning activities and peer interactions.Conclusion Occupational therapists require an up-to-date knowledge base and specialized tools to assess this population. Strategies to translate existing standardized performance-based instruments into practice may contribute to meeting these needs.

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 score0.170
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.238
GPT teacher head0.557
Teacher spread0.319 · 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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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