Self-harm and rurality in Canada: an analysis of hospitalization data from 2015 to 2019
Bibliographic record
Abstract
PURPOSE: The incidence of self-harm is an important indicator in suicide surveillance and a target outcome for suicide prevention. Self-harm rates vary by geographic location and rurality appears to be a risk factor. The objectives of this study were to estimate rates of self-harm hospitalization in Canada over a 5-year period by sex and age group, and examine relationships between self-harm and rurality. METHODS: Hospitalizations related to self-harm were identified in a national dataset (the Discharge Abstract Database) for all patients aged 10 years or older who were discharged from hospital between 2015 and 2019. Self-harm hospitalization rates were calculated and stratified by year, sex, age group, and level of rurality, as measured using the Index of Remoteness. A Poisson regression was fit to estimate rate ratios for the levels of rurality. RESULTS: Rates of self-harm hospitalization were higher for females than males across all levels of rurality and increased with each level for both sexes, except for among young males. The widest rural-to-urban disparities were observed for the 10-19 and 20-34-year old age groups. Females aged 10-19 in very remote areas had the highest self-harm hospitalization rate. CONCLUSION: The rate of self-harm hospitalization in Canada varied by sex, age group, and level of rurality. Clinical and community-based interventions for self-harm, such as safety planning and increased access to mental health services, should be tailored to the differential risks across geographic contexts.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".