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Record W4302045305 · doi:10.37808/jhhsa.45.1.2

Specialized Inpatient Unit for Adults with Co-Morbid Mental Illness, Intellectual Disability, and Challenging Behavior

2022· article· en· W4302045305 on OpenAlexaff
Kelly Vinquist, Jess G. Fiedorowicz, Jodi Tate

Bibliographic record

VenueJournal of Health and Human Services Administration · 2022
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIrritabilityChallenging behaviourMental illnessSpecialtyPolypharmacyPsychiatryIntellectual disabilityMedicineLethargyChecklistMental healthPsychologyCognitionIntensive care medicine

Abstract

fetched live from OpenAlex

Individuals with intellectual disability (ID) often experience barriers accessing appropriate and effective healthcare. Our specialty program was established to provide interdisciplinary services across the continuum of care to patients with ID, co-morbid mental illness, and challenging behaviors by identifying the etiology of challenging behaviors and creating a comprehensive plan for treatment. We describe the structure of our inpatient specialty unit and a retrospective chart review completed to evaluate the combined impact on the prevalence of challenging behaviors as measured by the Aberrant Behaviors Checklist (ABC) and polypharmacy. Fifty-five patients were evaluated before and one month following 72 corresponding hospitalizations. Mean ABC scores improved from 69.5 to 54.7 (p=.02) with decreases in subscales for irritability, lethargy, hyperactivity, and inappropriate speech but not stereotypy. Total psychotropic medication use was reduced from a mean of 4.9 to 3.8 medications (p<.001). Our specialized interdisciplinary treatment approach appears to benefit many patients with complex 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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.057
GPT teacher head0.372
Teacher spread0.316 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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