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Record W4387888489 · doi:10.18332/ejm/172485

Empathy and mental resilience evaluation after completion of the generic instructor course of the European Resuscitation Council

2023· article· en· W4387888489 on OpenAlexfundno aff
Styliani Paliatsiou, Theodoros Xanthos, Ioannis Zervas, Paraskevi Volaki, Nicoletta Iacovidou

Bibliographic record

VenueEuropean Journal of Midwifery · 2023
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
FundersCentrum fÖr Personcentrerad VårdStockholms Läns LandstingSocial Sciences and Humanities Research Council of CanadaInstitut National d'assurance Maladie-InvaliditéVlaamse regeringPetroleum Technology Development FundHellenic Foundation for Research and InnovationHaute école Spécialisée de Suisse OccidentaleVedecká Grantová Agentúra MŠVVaŠ SR a SAVGöteborgs UniversitetTürkiye Bilimsel ve Teknolojik Araştırma KurumuCity, University of LondonVetenskapsrådetLaerdal Foundation for Acute MedicineSuomalainen Lääkäriseura DuodecimAmasya ÜniversitesiKnut och Alice Wallenbergs StiftelseKarolinska InstitutetOpetushallitusEuropean CommissionUK Research and InnovationInnovationsfondenBundesministerium für Bildung und ForschungFinska LäkaresällskapetRoyal College of Midwives
KeywordsCourse (navigation)Resilience (materials science)EmpathyResuscitationMedical educationPsychologyMedicinePsychiatryEngineeringEmergency medicine

Abstract

fetched live from OpenAlex

reports of data from original research.• Review Papers -comprehensive, authoritative, reviews within the journal's scope.• Short Reports -brief reports of data from original research.• Methodology Papers -Papers that present different methodological approaches that can be used to investigate problems in a relevant scientific field and to encourage innovation.• Policy Case Studies -brief articles on policy development at a regional or national level.• Study Protocols -articles describing a research protocol of a study.

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)0.005
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.074
GPT teacher head0.341
Teacher spread0.267 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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