Hands-On Practice on Sustainable Simulators in the Context of Training for Rural and Remote Practice Through a Fundamental Skills Workshop
Bibliographic record
Abstract
Simulation-based education (SBE) is a sustainable method to allow healthcare professionals to develop competencies in clinical skills that can be difficult to maintain in rural and remote settings. Simulation-based skills training is necessary for healthcare professionals that experience difficulties accessing skills development and maintenance courses to address the needs of rural communities. However, simulators, a key element of simulation, are often prohibitively expensive and follow a "one-size-fits-all" approach. Using additive manufacturing (AM) techniques, more specifically three-dimensional (3D) printing, to produce inexpensive yet functional and customizable simulators is an ideal solution for learners to practice and improve their procedural skills anywhere and anytime. AM allows for the customization of simulators to fit any context while reducing costs and is an economic solution that moves away from the use of animal products to a more ethical, sustainable method for training. This technical report describes the delivery of a fundamental skills workshop to provide hands-on training to rural and remote healthcare professionals using 3D-printed simulators purposefully designed following design-to-cost principles. The workshop was delivered at a three-hour session hosted at a rural and remote medicine course in Ottawa, Canada. The workshop consisted of four technical skills: suturing, cricothyrotomy, episiotomy, and intraosseous infusion (tibial) (IO) and used a blended learning approach to train healthcare professionals and trainees who practice in rural and remote areas. In addition, the learners were granted access to a custom-designed learning management system, which provided a repository of instructional materials, and enabled them to record and upload personal practice sessions, review other learners' practice sessions, collaborate, and provide feedback to other learners. The feedback collected from participants, instructors, and observations on the delivery of the workshop will help improve the structure and training provided to learners. The delivery of this workshop annually is an ideal solution to ensure parsimonious delivery of simulation training for rural and remote healthcare professionals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".