Young children's ability to report on past, future, and hypothetical pain states: a cognitive-developmental perspective
Bibliographic record
Abstract
Children are at times asked by clinicians or researchers to rate their pain associated with their past, future, or hypothetical experiences. However, little consideration is typically given to the cognitive-developmental requirements of such pain reports. Consequently, these pain assessment tasks may exceed the abilities of some children, potentially resulting in biased or random responses. This could lead to the over- or under-treatment of children's pain. This review provides an overview of factors, and specifically the cognitive-developmental prerequisites, that may affect a child's ability to report on nonpresent pain states, such as past, future, or hypothetical pain experiences. Children's ability to report on past pains may be influenced by developmental (age, cognitive ability), contextual (mood state, language used by significant others), affective and pain-related factors. The ability to mentally construct and report on future painful experiences may be shaped by memory of past experiences, information provision and learning, contextual factors, knowledge about oneself, cognitive coping style, and cognitive development. Hypothetical pain reports are sometimes used in the development and validation of pain assessment scales, as a tool in assessing cognitive-developmental and social-developmental aspects of children's reports of pain, and for the purposes of training children to use self-report scales. Rating pain associated with hypothetical pain scenarios requires the ability to recognize pain in another person and depends on the child's experience with pain. Enhanced understanding of cognitive-developmental requirements of young children's pain reports could lead to improved understanding, assessment, and treatment of pediatric pain.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".