The relationship between rurality, travel time to care and death by suicide
Bibliographic record
Abstract
BACKGROUND: We previously found an association between rurality and death by suicide, where those living in rural areas were more likely to die by suicide. One potential reason why this relationship exists might be travel time to care. This paper examines the relationship between travel time to both psychiatric and general hospitals and suicide, and then determine whether travel time to care mediates the relationship between rurality and suicide. METHODS: This is a population-based nested case-control study. Data from 2007 to 2017 were obtained from administrative databases held at ICES, which capture all hospital and emergency department visits across Ontario. Suicides were captured using vital statistics. Travel time to care was calculated from the resident's home to the nearest hospital based on the postal codes of both locations. Rurality was measured using Metropolitan Influence Zones. RESULTS: For every hour in travel time a male resides from a general hospital, their risk of death by suicide doubles (AOR = 2.08, 95% CI = 1.61-2.69). Longer travel times to psychiatric hospitals also increases risk of suicide among males (AOR = 1.03, 95%CI = 1.02-1.05). Travel time to general hospitals is a significant mediator of the relationship between rurality and suicide among males, accounting for 6.52% of the relationship between rurality and increased risk of suicide. However, we also found that there is effect modification, where the relationship between travel time and suicide is only significant among males living in urban areas. CONCLUSIONS: Overall, these findings suggest that males who must travel longer to hospitals are at a greater risk of suicide compared to those who travel a shorter time. Furthermore, travel time to care is a mediator of the association between rurality and suicide among males.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".