The impact of a cancer diagnosis on nonfatal self-injury: a matched cohort study in Ontario
Bibliographic record
Abstract
BACKGROUND: Psychological distress following a cancer diagnosis potentially increases the risk of intentional, nonfatal self-injury. The purpose of this work is to evaluate and compare rates of nonfatal self-injury among individuals in Ontario diagnosed with cancer against matched controls with no history of cancer. METHODS: Adults in Ontario diagnosed with cancer from 2007 to 2019 were matched to 2 controls with no history of cancer, based on age and sex. We calculated the absolute and relative difference in rates of nonfatal self-injury in the 5 years before and after the index date (date of cancer diagnosis and dummy date for controls). We used crude difference-in-differences methods and adjusted Poisson regression-based analyses to examine whether the change in rates of nonfatal self-injury before and after index differed between cancer patients and controls. RESULTS: The cohort included 803 740 people with cancer and 1 607 480 matched controls. In the first year after diagnosis, individuals with cancer had a 1.17-fold increase in rates of nonfatal self-injury (95% confidence interval [CI] 1.03-1.33) compared with matched controls, after accounting for pre-existing differences in rates of nonfatal self-injury and other clinical characteristics between the groups. Rates of nonfatal self-injury remained elevated in the cancer group by 1.07-fold for up to 5 years after diagnosis (95% CI 0.95-1.21). INTERPRETATION: In this study, incidence of nonfatal self-injury was higher among individuals diagnosed with cancer, with the greatest impact observed in the first year after diagnosis. This work highlights the need for robust and accessible psychosocial oncology programs to support mental health along the cancer journey.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".