Psychological Factors Influencing Pain Intensity Perception: A Qualitative Study on Canadian Patient Insights
Bibliographic record
Abstract
Background: Chronic pain is a pervasive condition that significantly impacts the quality of life and involves complex interactions between physical sensations and psychological factors. This study aimed to explore the psychological factors influencing pain intensity perception, offering insights into how emotional responses, cognitive perceptions, social influences, and physical experiences shaping the experience of pain. Methods: This qualitative study involved semi-structured interviews with 28 participants aged 18-65 from Richmond Hill, Ontario, who experience chronic pain within September to December 2023. The interviews aimed to achieve theoretical saturation and were analyzed using NVivo software to identify themes and sub-themes within the data. Results: Four main themes were identified: emotional responses, cognitive perceptions, social influences, and physical experience. Each theme comprised several categories with distinct concepts such as anxiety, depression, coping mechanisms, pain significance, personal control, family dynamics, healthcare interactions, sensory details, and activity levels. These themes collectively depicted a comprehensive view of the multifaceted psychological impact on pain perception. Conclusion: The study underscores the importance of addressing the psychological aspects of pain perception in chronic pain management. By integrating emotional, cognitive, and social factors, healthcare providers can enhance therapeutic approaches and improve quality of life for individuals suffering from chronic pain.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".