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Record W1518963221 · doi:10.18192/uojm.v4i1.1039

Dying young: Excess morbidity and mortality in individuals with severe mental illness and what we should be doing about it

2014· article· en· W1518963221 on OpenAlexaffvenue
Darya Kurowecki, Justin Godbout

Bibliographic record

VenueUniversity of Ottawa Journal of Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineMental illnessLife expectancyPsychiatryDiseaseSchizophrenia (object-oriented programming)PopulationMental healthQuality of life (healthcare)Bipolar disorderMortality rateIncidence (geometry)ObesityEtiologyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

“We talk about people with mental illness, and people with diabetes, and smokers and the obese, and so on and so on. We’re talking about the same people – just with different labels.”– Health care professional [1, p. 6]Severe mental illness (SMI) most commonly refers to mental disorders with a psychotic component and significantly reduced functioning despite the presence of inherent differences in risk factors, etiologies, and treatments [1]. The most common disorders that fall under this term include schizophrenia and bipolar disorder [1]. Over a decade of research into the morbidity and mortality of individuals with SMI has consistently revealed mortality rates two to three times higher and a life expectancy of 25-30 years shorter compared to the general population [1-4]. Contrary to popular belief, the main causes of early death are not drug overdose or suicide, but rather, preventable illnesses such as cardiovascular disease, diabetes, and HIV/AIDS [1,3,5-7]. Incidence of other preventable conditions, such as obesity and respiratory disease, is also much higher among patients with SMI, and when present, is associated with a more severe course of mental illness and a reduced quality of life [3,8]. Such findings bring significant questions: what is the cause of this disparity in mortality/ morbidity? What can health care professionals do to help reduce this gap?A recent report by the Early Onset Illness and Mortality Working Group [1] outlines several factors that may contribute to poor physical health of people with SMI. Some factors, such as those related to the mental illness itself (e.g., cognitive impairment, a lack of communication skills, medication side-effects) and socioeconomic status (e.g., poverty, poor education) may be less amenable to modification, but should nevertheless be a target for action. Other contributing factors include behaviour and lifestyle (e.g., physical inactivity, obesity, tobacco smoking), and poor preventative medical care (e.g., disparity in quality of medical care), both of which are more easily modifiable with the assistance of medical care practitioners. Here we will summarize the factors responsible for poor physical health in SMI, specifically focusing on the mental illness itself, socioeconomic status, behaviour and lifestyle, health care system barriers, and insufficient preventative medical care. We will then propose future directions and ways in which medical students and current medical professionals can help reduce this gap.

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 score0.028
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.034
GPT teacher head0.296
Teacher spread0.262 · 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

Citations2
Published2014
Admission routes2
Has abstractyes

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