The Relationship Between Chronic Pain, Depression, Psychosocial Factors, and Suicidality in Adolescents
Bibliographic record
Abstract
BACKGROUND: Chronic pain in youth is often associated with social conflict, depression, and suicidality. The interpersonal theory of suicide posits that there are psychosocial factors, such as peer victimization and lack of fear of pain, that may also influence suicidality. OBJECTIVES: The objective of this study was to determine whether depressive symptoms, peer victimization, and lack of fear of pain predict suicidality in adolescents with chronic pain. It was hypothesized that higher levels of depressive symptoms and peer victimization, and lower levels of fear of pain, would predict a higher lifetime prevalence of suicidality. METHODS: Participants consisted of 184 youth with primary chronic pain conditions (10 to 18 y, M = 14.27 y). Measures included diagnostic clinical interviews assessing suicidality and self-report questionnaires assessing depressive symptoms, peer victimization, and fear of pain. RESULTS: Forty-two (22.8%) participants reported suicidality. Regression analyses demonstrated that the occurrence of suicidality was associated with higher rates of depressive symptoms (β = 1.03, P = 0.020, 95% CI: 1.01, 1.06) and peer victimization (β = 2.23, P < 0.05, 95% CI: 1.07, 4.63), though there was no association between lower fear of pain and suicidality. DISCUSSION: These results suggest that depressive symptoms and peer victimization are significant predictors of suicidality in adolescents with chronic pain; however, lower fear of pain was not shown to be a significant predictor. Given these findings, depression and peer victimization should be further explored and considered in the design and implementation of prevention and early intervention strategies that target chronic pain and suicidality in youth.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".