Design of an Intramuscular Injection Simulator: Accommodating Cultural Differences
Bibliographic record
Abstract
We had developed an inexpensive intramuscular (IM) injection simulator and gathered feedback from Canadian hospital-based practicing nurses about the design features of the simulator. While the feedback critiqued the density of the simulator as being too stiff and suggested making the shape more realistic, it was also unanimously agreed that this IM injection simulator is more realistic than any other previous models they have used, therefore deeming it an acceptable training tool for nursing students in Canada. For this simulator to serve as a training tool in other countries, such as Singapore, we partnered with SingHealth, a hospital network in Singapore, to conduct identical product testing in a different ethnic context and compare the data to our previous work. This article is based on this study. We had 21 nurses from Singapore General Hospital test the IM injection simulator and fill out the same survey the Canadian nurses had done. With a 100% response rate, only 26% of the Singapore hospital-based nurses agreed that this IM injection simulator is a more ethnically appropriate representation of anatomy than previous simulators they have used. There were numerous other differences in feedback compared to the Canadian nurses, such as the fat layer being too thick. These differences in feedback highlight the importance of including ethnicity as a factor during the design of simulators. Therefore, despite the silicone IM injection simulator being a cost-effective solution to practice IM injections, the features of the simulator need to be improved to make it a valuable teaching tool for nursing students, especially those in Singapore.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".