Injection Considerations for Administering Endocrine Parenteral Therapies to Children
Bibliographic record
Abstract
Introduction Early diagnosis of chronic endocrine conditions requiring injectable treatment is common, and injections may cause children to experience uncomfortable feelings ( e.g ., pain and anxiety). No recent publications have comprehensively described good nursing practices for pediatric injections. This review identifies key factors that can facilitate improved injection techniques and ideal treatment options for children, with a focus on endocrine parenteral therapies. Methods PubMed, Embase, and CINAHL were searched. MeSH search terms included “child,” “injections/methods,” “pain/prevention and control,” “pain management/methods,” “patient positioning,” and “analgesic/therapeutic use.” Literature related to interventions pediatric nurses can implement to decrease injection-related discomfort and other treatment factors that can minimize injection-related pain was included. Results Nurses should build a strong rapport with the child and/or caregiver, as this can improve the expectations for the injection experience. Nurses can assist caregivers with selecting the most appropriate treatment and provide options that may reduce out-of-pocket costs. Before administering an injection, nurses should check the “Five Rights,” assess skin to select an appropriate injection site, and prepare skin for injection. Good injection techniques include: administration of room temperature medications, comforting positioning and holding of the child by the caregiver, distraction activities, and local anesthetic agents. In addition to using good pediatric injection techniques, nurses can partner with Certified Child Life Specialists when available to help alleviate the child’s anxiousness. Discussion Pediatric nurses benefit from a broad understanding of pediatric injection techniques. Conclusion Optimal pre-, during, and post-injection techniques, along with proper education and care, can reduce pain and anxiety in children, improve caregivers’ experiences, and prevent treatment delays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".