Clinician perspectives concerning the treatment of adolescents with co-occurring chronic pain and mental health symptoms
Bibliographic record
Abstract
Pain and mental health symptoms frequently co-occur in adolescents, often posing physical, social, and emotional challenges. While previous research has focused on clinician perspectives on chronic pain in isolation, limited knowledge exists on the potential unique challenges these co-occurring symptoms' present to clinicians in providing appropriate support to adolescents. This study examined clinician perspectives on the challenges and barriers to treating adolescents who experience co-occurring pain and mental health symptoms. Using a cross-sectional qualitative online vignette survey, responses were collected from 40 clinicians, including psychologists, physiotherapists, and doctors involved in treating adolescents (11-19 years) who experience co-occurring chronic pain and mental health symptoms. Participants, recruited from several countries, were asked about their perceived challenges to treating adolescents with co-occurring chronic pain and mental health symptoms. Vignettes were analyzed using reflexive thematic analysis. The analysis generated two themes. The first, 'tangled threads', describes how clinicians perceive mistrust from the adolescents based on previous negative clinician encounters and a perceived need to 'undo' this anticipated harm. The second theme 'the difficult-to-pursue integrated approach' depicts how fragmentation and siloed services for pain and mental health hinder effective treatment for adolescents who experience both symptoms. Co-occurring pain and mental health symptoms in adolescents are often initially mismanaged because they do not fit the mould of the services available to treat them, resulting in a more complex presentation to clinicians. The development of a more integrated clinical approach to treating adolescents with co-occurring pain and mental health symptoms is needed. PERSPECTIVE: This study identifies that clinicians perceive they face challenges treating adolescents with co-occurring pain and mental health symptoms, often due to the adolescent's prior clinical experiences and the limited comprehensive treatment options available to them. An integrated approach is urgently needed to tailor care and reduce harm.
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".