Teddy and I Get a Check-Up: A Pilot Educational Intervention Teaching Children Coping Strategies for Managing Procedure-Related Pain and Fear
Bibliographic record
Abstract
Background. Pediatric medical information provision literature focuses on hospitalization and surgical procedures, but children would also benefit from an educational program regarding more commonly experienced medical procedures (e.g., needles, general check-up). Objective. To determine whether an evidence-based educational program reduced children's ratings of fear of and expected pain from medical stimuli and increased their knowledge of procedural coping strategies. Methods. An educational, interactive, developmentally appropriate Teddy Bear Clinic Tour was developed and delivered at a veterinary clinic. During this tour, 71 5-10-year-old children (Mage = 6.62 years, SD = 1.19) were taught about medical equipment, procedures, and coping strategies through modelling and rehearsal. In a single-group, pretest posttest design, participants reported their fear of and expected pain from medical and nonmedical stimuli. Children were also asked to report strategies they would use to cope with procedural fear. Results. Children's ratings for expected pain during a needle procedure were reduced following the intervention. No significant change occurred in children's fear of needles. Children reported more intervention-taught coping strategies at Time 2. Conclusions. The results of this study suggest that an evidence-based, interactive educational program can reduce young children's expectations of needle pain and may help teach them procedural coping strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".