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Record W4400211232 · doi:10.1089/jchc.23.06.0057

Predictors of Pain and Mood Disturbances Among Older People in Custody Using an interRAI Assessment

2024· article· en· W4400211232 on OpenAlexaffabout
Amanda Mofina, Charlene France, Gregory Brown, Samir K. Sinha, Dan Heurter, Navita Viveky, Sandra MacLeod, Micaela Jantzi, Nicoda Foster, John P. Hirdes

Bibliographic record

VenueJournal of Correctional Health Care · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsSinai Health SystemUniversity Health NetworkNipissing UniversityUniversity of TorontoUniversity of Waterloo
Fundersnot available
KeywordsMedicineMoodPsychiatryClinical psychology

Abstract

fetched live from OpenAlex

The population of people in federal custody in Canada is aging. Those in custody report experiencing poorer health and high rates of chronic health conditions. Two health concerns that are disproportionately higher among those in custody are mood disorders and pain. This cross-sectional study examined health indicators associated with pain and depressive symptoms among older people (50 years and above) from multiple facilities and security levels in federal custody in Canada. Participants were assessed using the interRAI Emergency Department Contact Assessment, which captures key health indicators. Chi square and logistic regression analyses were conducted to describe the population and identify health indicators associated with mood- and pain-related outcomes, respectively. Of the 1,422 participants in this study, the majority (55%) experienced pain and at least 1 out of 5 experienced depressive symptoms. Health indicators associated with depressive symptoms and/or pain were functional measures, including mobility, managing medication(s), and dyspnea. Depressive symptoms and pain are highly prevalent among older adults in federal custody. The relationship between functional health, depressive symptoms, and pain highlights the importance of interprofessional health care and biopsychosocial intervention(s).

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.369

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 science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.410
Teacher spread0.387 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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