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Record W1538532516 · doi:10.1111/jppi.12092

Assessment of an Expert Committee as a Referral Process Within Health and Social Services

2015· article· en· W1538532516 on OpenAlexaff
Audrée Tremblay, Diane Morin

Bibliographic record

VenueJournal of Policy and Practice in Intellectual Disabilities · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsUniversité du Québec à Montréal
FundersMinistère de la Santé
KeywordsReferralIntellectual disabilityHealth careTest (biology)PsychologyFamily medicineMedicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

Abstract In Q uébec, C anada, health and social services available to persons with intellectual disability are hierarchically organized into three levels of care: primary, secondary, and tertiary care. The referral processes through which persons gain access to services at each level vary across facilities. As a result, persons with intellectual disability may not receive the appropriate level of care and the responsibilities of facilities at each level overlap, creating an undue burden on the public healthcare system. This study sought to propose a structured assessment and referral method within the network of public services. Specifically, it evaluated the correspondence between the currently received level of care and an expert committee's determination. Furthermore, it examined client‐related variables that were associated with the level of services. An expert committee evaluated the level of specialization of services required by 30 persons with intellectual disability. The committee's determination was based on participants' files and presentations by their primary case worker. It was found that 10 out of the 30 participants were not receiving the level of care determined to be necessary by the expert committee. Challenging behaviors were most strongly associated with the committee's determination. This study underscores the primacy of clinical judgment, rather than a predetermined list of participant characteristics, in order to refer persons with intellectual disability toward services that best meet their specific needs. It also highlights the importance of taking into account challenging behaviors.

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)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.433
Teacher spread0.331 · 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 designQualitative
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

Citations3
Published2015
Admission routes1
Has abstractyes

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