Pediatrician Explanations of Pediatric Pain in Clinical Settings: A Delicate Craft
Bibliographic record
Abstract
Explaining chronic pain to children and families can be challenging, particularly in the absence of an obvious physiologically identifiable cause for the child's pain. In addition to medical intervention, children and families may expect clinicians to provide clarity around the cause of pain. Such explanations are often provided by clinicians who have not received formal pain training. This qualitative study sought to explore the following question: What do pediatricians consider to be important when providing pain explanations to children and their parents? Using semistructured interview methods, 16 UK pediatricians were interviewed regarding their perceptions of explaining chronic pain to children and families in clinical settings. Data were analyzed using inductive reflexive thematic analysis. Analyses generated 3 themes: 1) timing of the explanation, 2) casting a wider net, and 3) tailoring of the narrative. Study findings demonstrated the need for pediatricians to skilfully interpret where children and families are in their pain journey and deliver an appropriate and adaptable explanation relating to individual needs. Analyses identified the importance of providing a pain explanation that could be repeated and understood by others outside the consultation room, to enable children and families to accept the explanation. PERSPECTIVE: Study findings identify the importance of language in addition to familial and broader factors that may influence the provision and adoption of chronic pain explanations provided by pediatricians to children and families. Improving pain explanation provision may influence treatment engagement for children and their parents, subsequently impacting pain related outcomes.
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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 | 0.030 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".