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Record W2361993614 · doi:10.1080/19315864.2015.1108377

Occurrence of Medical Concerns in Psychiatric Outpatients with Intellectual Disabilities

2016· article· en· W2361993614 on OpenAlexaff
Kousha Azimi, Miti Modi, Janice Hurlbut, Yona Lunsky

Bibliographic record

VenueJournal of Mental Health Research in Intellectual Disabilities · 2016
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsCentre for Addiction and Mental HealthQueen's University
Fundersnot available
KeywordsPsychiatryPhysical healthMedicineMental healthIntellectual disabilityPsychologyNursing

Abstract

fetched live from OpenAlex

Despite the fact that adults with both intellectual disabilities (ID) and psychiatric disorders are at increased risk for physical health problems, few studies have described their medical concerns specifically. This study reports on the rates of physical health issues and completion of recommended health screenings among 78 adult outpatients with ID in a specialized psychiatric service. We conducted a retrospective chart review of physical health information gathered by the psychiatric nurse using a standardized head-to-toe assessment tool and compared findings from nursing assessments to health information collected at intake based on patient and caregiver perspectives. Psychiatric outpatients with ID had at least one physical health issue and most had multiple concerns flagged through nursing assessments, the majority of which were not documented at intake. Our findings highlight the important role of the psychiatric nurse in management of patients with ID and indicate the need for more comprehensive health monitoring.

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)0.005
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.242
GPT teacher head0.486
Teacher spread0.244 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

Explore more

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