Designing Interactive CPR Manikins for Children: QUT Industrial Design in Collaboration With Clinical Skills Development Services, Metro North Hospital And Health Services
Bibliographic record
Abstract
This project aimed to equip future generations and the broader community with practical lifesaving skills through interactive devices that expedite learning and are accessible from a cost-effective and local manufacturing perspective. In the case of an emergency, children are one of the most vulnerable groups in a community. Yet, it has also been demonstrated that children’s imagination, intuitiveness and out of the box thinking, has helped them and others problem-solve their way out of life-threatening situations. Can we as industrial designers develop solutions that help prepare the future generations with practical lifesaving skills? Simulators or clinical manikins required to teach CPR techniques are expensive. The community access to training is through specialised training services (e.g., Red Cross training), and it is also expensive. There are several types of manikin designs for clinical and training use; most of them are imported or fabricated in Australia at a higher cost with components sourced from overseas. This project started considering the viability of local manufacturing CPR Manikins with the intention of lowering the cost and making them widely available to the community for CPR training. With this in mind, this project is an exploration within an Industrial Design unit that focused on the design of a child-size manikin to teach children the technique. The project involved an international collaboration with York University in Canada and THI Ingolstadt University in Germany, under the umbrella of a GLOBAL DESIGN STUDIO to further explore students' learning experiences of developing these interactive designs in digital and remote peer learning groups.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".