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Record W2402201187 · doi:10.1111/1742-6723.12606

Potential role for psychological skills training in emergency medicine: Part 1 ‐ Introduction and background

2016· review· en· W2402201187 on OpenAlexaff
Michael Lauria, Stephen Rush, Scott D. Weingart, Jason Brooks, Isabelle Gallo

Bibliographic record

VenueEmergency Medicine Australasia · 2016
Typereview
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsUniversity of Manitoba
FundersDartmouth College
KeywordsMedicineTraining (meteorology)Medical educationAthletesRelaxation (psychology)CognitionPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Psychological skills training (PST) is the systematic acquisition and practice of different psychological techniques to improve cognitive and technical performance. This training consists of three phases: education, skills acquisition and practice. Some of the psychological skills developed in this training include relaxation techniques, focusing and concentration skills, positive 'self-suggestion' and visualisation exercises. Since the middle of the 20th century, PST has been successfully applied by athletes, performing artists, business executives, military personnel and other professionals in high-risk occupations. Research in these areas has demonstrated the breadth and depth of the training's effectiveness. Despite the benefits realised in other professions, medicine has only recently begun to explore certain elements of PST. The present paper reviews the history and evidence behind the concept of PST. In addition, it presents some aspects of PST that have already been incorporated into medical training as well as implications for developing more comprehensive programmes to improve delivery of emergency medical care.

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)0.002
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.732
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0240.000

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 head0.105
GPT teacher head0.450
Teacher spread0.345 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations18
Published2016
Admission routes1
Has abstractyes

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