The experiences of people diagnosed with severe mental illness and colorectal cancer: A qualitative study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".