Pain Catastrophizing: Controversies, Misconceptions and Future Directions
Bibliographic record
Abstract
Recent reports have pointed to problems with the term "pain catastrophizing." Critiques of the term pain catastrophizing have come from several sources including individuals with chronic pain, advocates for individuals with chronic pain, and pain scholars. Reports indicate that the term has been used to dismiss the medical basis of pain complaints, to question the authenticity of pain complaints, and to blame individuals with pain for their pain condition. In this paper, we advance the position that the problems prompting calls to rename the construct of pain catastrophizing have little to do with the term, and as such, changing the term will do little to solve these problems. We argue that continued calls for changing or deleting the term pain catastrophizing will only divert attention away from some fundamental flaws in how individuals with pain conditions are assessed and treated. Some of these fundamental flaws have their roots in the inadequate training of health and allied health professionals in evidence-based models of pain, in the use of psychological assessment and intervention tools for the clinical management of pain, and in gender equity and antiracism. Critiques that pain scholars have leveled against the defining, operational, and conceptual bases of pain catastrophizing are also addressed. Arguments for reconceptualizing pain catastrophizing as a worry-related construct are discussed. Recommendations are made for remediation of the problems that have contributed to calls to rename the term pain catastrophizing. PERSPECTIVE: The issues prompting calls to rename the construct of pain catastrophizing have their roots in fundamental flaws in how individuals with pain are assessed and treated. Efforts to address these problems will require more than a simple change in terminology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.063 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".