Pediatric Fear‐Avoidance Model of Chronic Pain: Foundation, Application and Future Directions
Bibliographic record
Abstract
The fear-avoidance model of chronic musculoskeletal pain has become an increasingly popular conceptualization of the processes and mechanisms through which acute pain can become chronic. Despite rapidly growing interest and research regarding the influence of fear-avoidance constructs on pain-related disability in children and adolescents, there have been no amendments to the model to account for unique aspects of pediatric chronic pain. A comprehensive understanding of the role of fear-avoidance in pediatric chronic pain necessitates understanding of both child⁄adolescent and parent factors implicated in its development and maintenance. The primary purpose of the present article is to propose an empirically-based pediatric fear-avoidance model of chronic pain that accounts for both child⁄adolescent and parent factors as well as their potential interactive effects. To accomplish this goal, the present article will define important fear-avoidance constructs, provide a summary of the general fear-avoidance model and review the growing empirical literature regarding the role of fear-avoidance constructs in pediatric chronic pain. Assessment and treatment options for children with chronic pain will also be described in the context of the proposed pediatric fear-avoidance model of chronic pain. Finally, avenues for future investigation will be proposed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".