Pointing and calling the way to patient safety: An introduction and initial use case
Bibliographic record
Abstract
Background Using tools from outside healthcare can help improve patient safety. Pointing and Calling (Shisa Kanko) is an operational procedure developed for industry in Japan to prevent human error and has been used in healthcare in Asian countries to reduce errors during medication administration. Pointing and Calling affects cognitive task switching by pointing to a place or object and calling out the operation to be performed. Aim Conduct an initial use case to examine the willingness and ability of healthcare professionals in a Western country to use Pointing and Calling. Methods An observational initial use case was conducted with nineteen Advanced Care Paramedic students. Confidence, perceptions, and use of Pointing and Calling were measured during a simulated clinical scenario along with facilitator perceptions. Results After the simulation participants were confident in their ability to use Pointing and Calling, found the method to be beneficial, and indicated they would use Pointing and Calling in the future. Participants often used the method for tasks such as checking vitals. Aspects of the method requiring clarification and more training were identified. Facilitators indicated the method appeared beneficial during simulations and could be incorporated into existing curriculum. Conclusions The benefits of Pointing and Calling are readily apparent to students and facilitators and both groups are receptive to the method. Pointing and Calling is low risk with substantial potential benefits. With more education and training Pointing and Calling could be effectively implemented.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".