Chronic Pain Conditions and Suicidal Ideation and Suicide Attempts: An Epidemiologic Perspective
Bibliographic record
Abstract
OBJECTIVES: Investigations of the association between chronic pain conditions and suicidal ideation (SI) and suicide attempts (SA) have rarely taken the effect of mental disorders into account and have been limited by nonrepresentative samples. The present study used a large population-based sample to investigate the association between chronic pain conditions and SI and SA. METHODS: Data were from the Canadian Community Health Survey Cycle 1.2 public use file conducted by Statistics Canada from 2001 to 2002 (N=36,984; response rate 77%). Respondents were asked if they had been diagnosed with the following painful conditions: migraine, back problems, arthritis, and fibromyalgia. Respondents were assessed for past 12-month SI and SA. The Composite International Diagnostic Interview was used to assess Diagnostic and Statistical Manual of Mental Disorders-IV. RESULTS: After adjusting for sociodemographics, Axis I mental disorders and comorbidity (3 or more mental disorders), the presence of 1 or more chronic pain conditions was associated with both SI and SA. Among respondents with a mental disorder, comorbidity with 1 or more chronic pain conditions was also associated with SI and SA. In models adjusting for other painful conditions, migraine had the strongest link with SI and SA. DISCUSSION: This is the first study to demonstrate the association between several chronic pain conditions and SI and SA while adjusting for mental disorders in a nationally representative sample. Moreover, this study demonstrates that among individuals with a mental disorder, having a chronic pain condition significantly increased the association with SI and SA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".