Empowerment-based Teddy Bear Clinic for Pre-school Children: A Student-led Educational Project
Bibliographic record
Abstract
Nurses are at the forefront of providing health education for the general public and are leaders in developing health education programs for all ages. Research has shown that the pediatric population often experience anxiety surrounding common medical procedures. However, evidence-based health education has been shown to enhance self-management, increase knowledge, and decrease anxiety in the pediatric population. One such successful evidence-based health education approach designed for the pediatric population is the Teddy Bear Clinic. The purpose of this article is to report on the efficacy of a nursing student-led Teddy Bear Clinic designed to increase the awareness of common medical equipment and procedures in the pre-school pediatric population. This quality improvement project used a program evaluation design to assess the children's knowledge of common medical procedures and equipment. Participants were a convenience sample of 16 children aged 3-5 years old, attending one daycare center in a large city in Atlantic Canada. Findings showed that after participation in the clinic, the pre-schoolers reported a high level of knowledge of common medical equipment and procedures. This project shows that a Teddy Bear Clinic run by senior nursing students can promote community partnerships and enhance health knowledge in pre-school children.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".