Expressive dimensions of pain catastrophizing: A comparative analysis of school children and children with clinical pain ☆
Bibliographic record
Abstract
We investigated the role of the child's pain catastrophizing in explaining (1) children's self-reported tendency to verbally share their pain experience with others and (2) different dimensions of pain expression, as described by the mother and the father, including non-verbal and verbal communicative pain behaviour and protective pain behaviour. Participants were school children, children with chronic or recurrent pain, and their parents. The results showed that: (1) Pain catastrophizing was associated with children's greater self-acknowledged tendency to verbally share their pain experience with others. (2) Mothers and fathers perceived highly catastrophizing children to be more communicative about their pain. (3) The role of pain catastrophizing in the child's verbal sharing of pain experiences and in explaining expressive behaviour as rated by parents did not differ between the school children and children with recurrent and chronic pain. (4) Nevertheless, findings indicated marked differences between school children and the clinical sample. Children of the clinical sample experienced more severe pain, more pain catastrophizing, more protective pain behaviour, but less verbal communications about their pain. These results further corroborate the position that catastrophic thoughts about pain have interpersonal consequences. Findings are discussed in terms of the possible functions and effects upon others of pain catastrophizing and associated categories of pain behaviour.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".