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Record W4417259246 · doi:10.1177/10398562251406026

The experiences of people diagnosed with severe mental illness and colorectal cancer: A qualitative study

2025· article· en· W4417259246 on OpenAlexaff
Tessa‐May Zirnsak, Julia De Nicola, Steve Kisely, Dan Siskind, Melinda M. Protani, Lisa Brophy

Bibliographic record

VenueAustralasian Psychiatry · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsDalhousie University
FundersCancer Australia
KeywordsQualitative researchMental illnessFocus groupMental healthLived experienceInterpersonal communicationHealth professionals

Abstract

fetched live from OpenAlex

ObjectivesThere is evidence that people with severe mental illness (SMI) are more likely to be diagnosed at a later stage and die earlier from colorectal cancer (CRC). The purpose of this study is to understand the barriers to effective CRC diagnosis and treatment for this group.MethodsFive people (four people diagnosed with CRC and SMI and one carer for a person in this group) participated in interviews about their experience of CRC diagnosis and treatment. Interviews were analysed using NVivo to identify key themes.ResultsWe identified four key themes: diagnostic overshadowing, fear, practical access and interpersonal partnerships.ConclusionsParticipants in this study were less likely to have their healthcare needs met because of discrimination and unmet needs associated with their diagnosis of SMI. This is not a new finding - many other mental and physical health researchers have identified this problem. But this study enables people who have lived through the challenge of this experience to share their perspective. Consequently, we recommend that future research focus on practical strategies to minimise discrimination among health professionals and policymakers and identify solutions to address the disproportionate barriers people diagnosed with SMI encounter regarding CRC testing.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.955

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 head0.008
GPT teacher head0.323
Teacher spread0.315 · 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 designQualitative
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
Published2025
Admission routes1
Has abstractyes

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