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Record W1546605608 · doi:10.1155/2015/753873

Evaluating Adult’s Competency: Application of the Competency Assessment Process

2015· article· en· W1546605608 on OpenAlexafffund
Dominique Giroux, Sylvie Tétreault, Marie-Pier Landry

Bibliographic record

VenueInternational Journal of Alzheimer s Disease · 2015
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsUniversité Laval
FundersAlzheimer Society Research ProgramAlzheimer Society
KeywordsMedicineProcess (computing)Relevance (law)Service (business)Plan (archaeology)Medical educationCompetency assessmentCognitionNursingKnowledge managementApplied psychologyPsychologyPsychiatryComputer science

Abstract

fetched live from OpenAlex

Competency assessment of adults with cognitive impairment or mental illness is a complex process that can have significant consequences for their rights. Some models put forth in the scientific literature have been proposed to guide health and social service professionals with this assessment process, but none of these appear to be complete. A new model, the Competency Assessment Process (CAP), was presented and validated in other studies. This paper adds to this corpus by presenting both the CAP model and the results of a survey given to health and social service professionals on its practical application in their clinical practice. The survey was administered to 35 participants trained in assessing competency following the CAP model. The results show that 40% of participants use the CAP to guide their assessment and the majority of those who do not yet use it plan to do so in the future. A large majority of participants consider this to be a relevant model and believe that all interdisciplinary teams should use it. These results support the relevance of the CAP model. Further research is planned to continue the study of the application of CAP in healthcare facilities.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.102
GPT teacher head0.494
Teacher spread0.392 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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