The Development of an Intramuscular Injection Simulation for Nursing Students
Bibliographic record
Abstract
Intramuscular (IM) injections are preferred over subcutaneous injections for administering medicine such as epinephrine and vaccines as the muscle tissue contains an increased vascular supply that provides ideal absorption of the drug being administered. However, administering an IM injection requires clinical judgment when choosing the injection site, understanding the relevant anatomy and physiology as well as the principles and techniques for administering an IM injection. Therefore, it is essential to learn and perform IM injections using injection simulators to practice the skill before administering to a real patient. Current IM injection simulators either favor realism at the expense of standardization or are expensive but do not provide a realistic experience. Therefore, it is imperative to develop an inexpensive but realistic intramuscular injection simulator that can be used to train nursing students so that they can be prepared for when they enter the clinical setting. This technical report aims to provide an overview of the development of an inexpensive and realistic deltoid simulator geared to teach nursing students the skill of IM injections. After development, the IM simulators were tested and validated by practicing nurses. An 18-item survey was administered to the nurses, and results indicated positive feedback about the realism of the simulator, in comparison to previous models used, such as the Wallcur® PRACTI-Injecta Pads (Wallcur LLC, San Diego, CA). Feedback to improve the density of the simulator as well as the shape and size to make it a more realistic experience was provided.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".