The Influence of Loneliness on Pain Outcomes for Adolescents: A Cross-Sectional Survey
Bibliographic record
Abstract
Background: Loneliness, the perception that one's social relationships do not meet the desire for social connection, is a risk factor for poor mental and physical health. Adolescents with chronic pain experience higher rates of peer loneliness which persists over time. Previous studies used a single loneliness measure, limiting our understanding of the nature of their loneliness. This study describes the types of peer loneliness (intimate, relational, and collective) experienced by these adolescents and the impact that peer loneliness has on pain-related outcomes. Methods: A cross-sectional online survey was completed by 128 Canadian adolescents aged 12-18 years who experienced pain for at least 3 months. Validated measures captured demographics, pain-related characteristics, types of peer-related loneliness, measures of social well-being, and mental and physical health outcomes. Results: Friedman's tests of z-scores indicate that participants equally experienced dyadic, relational, and collective peer loneliness. MANCOVA revealed that those who identify as Black were lonelier after controlling for socioeconomic status. Multiple regression showed that loneliness was a robust predicter of worse scores on social well-being and mental health outcomes with males and females equally impacted by loneliness. Despite moderate correlations between loneliness and pain interference and pain intensity, loneliness did not predict school absences, suggesting that loneliness' influence on physical pain outcomes may be temporally earlier (e.g. contribute to pain chronification). Conclusions: Peer loneliness among adolescents with chronic pain negatively impacts their social well-being and mental health outcomes. Interventions addressing loneliness to target all three types of peer loneliness may be key to improving pain-related outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".