Impact of Threat Level, Task Instruction, and Individual Characteristics on Cold Pressor Pain and Fear among Children and Their Parents
Bibliographic record
Abstract
The cold pressor task (CPT) is increasingly used to induce experimental pain in children, but the specific methodology of the CPT is quite variable across pediatric studies. This study examined how subtle variations in CPT methodology (eg. provision of low- or high-threat information regarding the task; provision or omission of maximum immersion time) may influence children's and parents' perceptions of the pain experience. Forty-eight children (8 to 14 years) and their parents were randomly assigned to receive information about the CPT that varied on 2 dimensions, prior to completing the task: (i) threat level: high-threat (task described as very painful, high pain expressions depicted) or low-threat (standard CPT instructions provided, low pain expressions depicted); (ii) ceiling: informed (provided maximum immersion time) or uninformed (information about maximum immersion time omitted). Parents and children in the high-threat condition expected greater child pain, and these children reported higher perceived threat of pain and state pain catastrophizing. For children in the low-threat condition, an informed ceiling was associated with less state pain catastrophizing during the CPT. Pain intensity, tolerance, and fear during the CPT did not differ by experimental group, but were predicted by child characteristics. Findings suggest that provision of threatening information may impact anticipatory outcomes, but experienced pain was better explained by individual child variables.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".