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Impact of training in Advanced Cardiac Life Support (ACLS) in the professional career and work environment

2018· article· en· W2794397762 on OpenAlexaff
Lunia Sofia Lima Azevedo, Lucas Gaspar Ribeiro, André Schmidt, Antônio Pazin‐Filho

Bibliographic record

VenueCiência & Saúde Coletiva · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsImpact
Fundersnot available
KeywordsAdvanced cardiac life supportGraduation (instrument)MedicineEarningsCertificationPopulationMedical educationPsychologyFamily medicineGerontologyCardiopulmonary resuscitationEmergency medicineManagementBusiness

Abstract

fetched live from OpenAlex

We sought to evaluate the impact of Advanced Cardiac Life Support (ACLS) training in the professional career and work environment of physicians who took the course in a single center certified by the American Heart Association (AHA). Of the 4631 students (since 1999 to 2009), 2776 were located, 657 letters were returned, with 388 excluded from the analysis for being returned lacking addressees. The final study population was composed of 269 participants allocated in 3 groups (< 3 years, 3-5 and > 5years). Longer training was associated with older age, male gender, having undergone residency training, private office, greater earnings and longer time since graduation and a lower chance to participate in providing care for a cardiac arrest. Regarding personal change, no modification was detected according to time since taking the course. The only change in the work environment was the purchase of an automated external defibrillator (AED) by those who had taken the course more than 5 years ago. In multivariable analysis, however, the implementation of an AED was not independently associated with this group, which showed a lower chance to take a new ACLS course. ACLS courses should emphasize also how physicians could reinforce the survival chain through environmental changes.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.296
Teacher spread0.276 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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