Innovative Program to Prevent Pediatric Chronic Postsurgical Pain: Patient Partner Feedback on Intervention Development
Bibliographic record
Abstract
BACKGROUND: The risk of developing chronic postsurgical pain (CPSP) in youth is related to psychological factors, including preoperative anxiety, depression, patient/caregiver pain catastrophizing, and poor self-efficacy in managing pain. While interventions exist to address these factors, they are generally brief and educational in nature. The current paper details patient partner feedback on the development of a psychologist-delivered perioperative psychological program (PPP) designed to identify and target psychological risk factors for CPSP and improve self-efficacy in managing pain. METHODS: Qualitative interviews were conducted with two patients and their caregivers to discuss their surgical and pain management experience and to advise on components of the PPP. RESULTS: Reflexive thematic analysis of interviews generated the following themes, which were incorporated into the content and implementation of the PPP: caregiver involvement, psychological and physical strategies for pain management, biopsychosocial pain education, intervention structure, and supporting materials. CONCLUSIONS: The development of a novel psychologist-led PPP is a promising approach to mitigate mental health risks associated with pediatric CPSP and potentially boost postoperative outcomes and family wellbeing. Integrating patient partner feedback ensures that the PPP is relevant, acceptable, and aligned with the needs and preferences of the patients it is designed to serve.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".