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Record W2024158283 · doi:10.1108/13660750310486721

Listening to consumers ... CARF Canada opens

2003· article· en· W2024158283 on OpenAlexaboutno aff
Daniel Stavert, Brian J. Boon

Bibliographic record

VenueLeadership in Health Services · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsnot available
Fundersnot available
KeywordsAccreditationBusinessGovernment (linguistics)CommissionService (business)MarketingHealth careAccountabilityPublic relationsMedicinePolitical scienceMedical educationFinance

Abstract

fetched live from OpenAlex

The Commission on Accreditation of Rehabilitation Facilities (CARF) has experienced considerable market growth in recent years. Growth has occurred in the health care industry with exceptional growth occurring in the fields of persons with disabilities and children’s services. Expansion of their services beyond the American boarders has resulted in CARF accrediting organizations in Canada, Ireland and Sweden with active work occurring in Denmark, Finland, France, Scotland, Italy, England and Australia. In Canada, policy makers at all levels of government began demanding greater community involvement in consumer service delivery. Policy makers and consumers made it clear that a system of accountability needed to be incorporated to ensure quality of service. In order to address the resulting growth in Canada and listen to the needs of consumers it became apparent that a separate office was required to meet the unique needs of Canadians. CARF Canada was established to meet the 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.417

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0380.009
Scholarly communication0.0140.005
Open science0.0020.007
Research integrity0.0130.012
Insufficient payload (model declined to judge)0.1250.020

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 head0.214
GPT teacher head0.381
Teacher spread0.168 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2003
Admission routes1
Has abstractyes

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