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Record W2786291121 · doi:10.1097/sga.0000000000000251

Predictors of Impaired Mental Health and Support Seeking in Adults With Inflammatory Bowel Disease

2018· article· en· W2786291121 on OpenAlexaboutno aff
Simon R. Knowles, Jane M. Andrews, Anna Porter

Bibliographic record

VenueGastroenterology Nursing · 2018
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthMedicineLogistic regressionCohortDiseasePsychiatryDistressClinical psychologyPsychologyGerontologyInternal medicine

Abstract

fetched live from OpenAlex

This study explored the possible factors associated with psychological distress in adults with inflammatory bowel disease (IBD) and also engagement in mental health services (MHS) in those reporting distress in a large Australian cohort. Participants with IBD completed an online survey assessing perceived IBD activity (Manitoba Index; MI), mental health status (K10), demographic details, and engagement with MHS for IBD-associated issues. Of 336 participants, 76.5% perceived themselves as having active disease over the past 6 months, and on K10 scores, 51.8% had a mental health issue. Of participants with a mental health issue, only 21.3% were currently receiving mental health support. A stepwise logistic regression analysis correctly classified 78.7% of the status of receiving mental health support, with lower income (<$60,000 per annum) the only significant predictor. Paradoxically, the degree of psychological distress did not correlate with seeking mental health support. The data show that in individuals with ongoing symptoms attributed to active IBD, mental health issues are highly prevalent, with older age and higher income being additional drivers of mental health issues. The greater challenge, however, seems not to be identifying mental health issues, but in getting those in need to engage in MHS.

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)0.000
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 score0.020
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.015
GPT teacher head0.333
Teacher spread0.318 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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