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Record W4410695891 · doi:10.1080/0142159x.2025.2508281

Teaching pointing and calling (Shisa Kanko) to reduce error and improve performance

2025· article· en· W4410695891 on OpenAlexaff
Efrem Violato, Mike Cheung

Bibliographic record

VenueMedical Teacher · 2025
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsNorthern Alberta Institute of Technology
Fundersnot available
KeywordsComputer sciencePsychologyMedical educationMedicine

Abstract

fetched live from OpenAlex

Paramedics operate in high-stakes, cognitively demanding environments where lapses in attention can jeopardize patient safety. While team-based communication strategies are commonly taught, there is a need for self-directed methods that support situational awareness and error prevention. 'Pointing and Calling' (P&C) is a Japanese technique that uses verbal and physical cues to heighten conscious attention and reduce mistakes. P&C was integrated into the Advanced Care Paramedic curriculum over three weeks covering conceptual instruction, guided practice through low-stakes activities, and application in high-fidelity simulations. Students employed P&C during critical tasks and received feedback during debriefings. Evaluation using the Kirkpatrick framework showed positive engagement, skill uptake, and transfer to other learning contexts. Several key lessons were identified for implementing training on P&C, including clarifying that P&C is a personal cognitive tool, not a directive to others. P&C's simplicity, low cost, and existing evidence support implementation across healthcare settings. P&C can be effective in low and high-resource environments alike. P&C represents a practical, scalable approach to improving patient safety.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.333
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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