Factors Associated with Suicidal Ideation in Patients with Chronic Non-Cancer Pain
Bibliographic record
Abstract
Objectives: This study’s aim was to identify the most important general and pain-related risk factors of suicidal ideation in a large sample of patients with chronic non-cancer pain. Methods: A total of 728 patients with chronic non-cancer pain were recruited from the waitlists of eight multidisciplinary pain clinics across Canada. Patients were assessed using self-administered questionnaires to measure demographic, pain-related (intensity, duration, interference, sleep problems), psychological (anxiety, anger, depressive symptoms including suicidal ideation), cognitive (catastrophizing, attitudes/beliefs), and health-related quality of life variables. A hierarchical logistic regression analysis was used to identify the factors that were associated with presence/absence of suicidal ideation while controlling for depressive symptoms. Results: The results showed that being a male, longer pain duration, higher anger levels, feelings of helplessness, greater pain magnification, and being more depressed were significant independent predictor factors of suicidal ideation, while better perceived mental health was related with a lesser likelihood of suicidal ideation. Moreover, being in a relationship and believing in a medical cure for pain might be protective of suicidal ideation while being anxious may be more associated with suicidal ideation. Conclusions: These results indicate that development of suicidal ideation is more closely related to pain chronicity and certain psychosocial factors than how severe or physically incapacitating the pain is. Many of these factors could potentially be modified by early identification of suicidal ideation and developing targeted cognitive interventions for suicidal at-risk patients. Research to examine the efficacy of these interventions for reducing suicidal ideation is warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".