Value for money in self‐regulated procedural simulation
Bibliographic record
Abstract
BACKGROUND: In self-regulated procedural simulation, learners practise on many simulators (e.g. paracentesis), self-regulating their choice of simulators, time and goals. Current needs assessments cannot predict the number of simulators needed to plan cost-effective self-regulated simulation. Knowing the ratios of simulators and participants would allow for better-informed purchase decisions to be made. METHODS: We designed 90-minute sessions of self-regulated procedural simulation for internal medicine residents. In Phase 1, 51 participants (8.5 per group) could use 22 simulators (US$69 925): ultrasound-guided central (n = 6) and peripheral (n = 2) venous catheterisation; thoracocentesis (n = 2); paracentesis (n = 2); lumbar puncture (n = 6); and arthrocentesis (n = 4). We calculated minimal numbers of simulators based on the time that participants used each simulator in order to design a resource-effective Phase 2, with 24 participants (with 12 per group) using 14 simulators (US$48 720) to meet their needs. RESULTS: Calculated from time of use (83 minutes in total), the optimal ratios of simulators expressed for 10 participants were 9.2: 3.7 for jugular and subclavian venous catheterisation (33 minutes); 1.5 for thoracocentesis (13 minutes), 1.0 for femoral venous catheterisation (9 minutes), 1.0 for lumbar puncture (9 minutes), 0.8 for peripheral venous catheterisation (8 minutes), 0.7 for paracentesis (6 minutes) and 0.5 for arthrocentesis (5 minutes). In Phase 2, the usage rate of simulators increased from 35.5% to 76.6%, maintaining the total time of use at 80.4 minutes. CONCLUSIONS: We present a replicable method for the cost-effective planning of self-regulated simulation by measuring the use of simulators. Expressed as ratios of simulators per participant, this information can support purchase decisions and be shared with similar programmes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".