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Record W1995129907 · doi:10.1176/appi.ps.60.4.538

Psychiatric Illness and Obesity: Recognizing the "Obesogenic" Nature of an Inpatient Psychiatric Setting

2009· article· en· W1995129907 on OpenAlex
Guy Faulkner, Paul Gorczynski, Tony Cohn

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePsychiatric Services · 2009
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychological interventionObesityPsychiatryPopulationMedicineWeight lossMental illnessWeight managementGerontologyPsychologyMental healthInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: The prevalence of obesity and obesity-related diseases is higher among individuals with psychiatric illness than in the general population. This study examined environmental factors that contribute to obesity in one psychiatric hospital in Canada. METHODS: Semistructured interviews were conducted with 25 key stakeholders from multiple professional disciplines at the hospital. Transcribed interviews were analyzed through content analysis with the analysis grid for environments linked to obesity (ANGELO) framework as a categorical template. RESULTS: Factors contributing to obesity in this setting were related to increased energy intake, such as easy access to high-calorie snacks and beverages, and reduced energy expenditure, such as lack of access to staircases. CONCLUSIONS: Psychiatric settings may contribute to the high prevalence of obesity among individuals with psychiatric illness. Ecologically framed interventions are required to address obesity in this population.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
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 head0.007
GPT teacher head0.279
Teacher spread0.272 · 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