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Discovering the Traits of Personality in Anesthesiologists at Different Stages of Emotional Burnout Syndrome

2020· article· en· W3108251843 on OpenAlexvenueno aff
Alexey Kokoshko, Aigul Abduldayeva, Nasrulla Shanazarov

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychological Treatments and Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsBurnoutAnesthesiologyBig Five personality traitsPersonalityPain medicineEmotional exhaustionMedicineIntensive carePsychologyNursingClinical psychologyPsychiatryIntensive care medicineSocial psychology

Abstract

fetched live from OpenAlex

To date, intensive care medicine, anesthesiology, and resuscitation are medical spheres that deal with lifesaving issues, particularly the treatment of patients in critical or life-threatening situations. The profession of anesthesiologist-resuscitator is relatively new, although it has centuries-old prehistory. The study aims to identify personality traits that predispose to the formation of emotional burnout syndrome in anesthesiologists-resuscitators. The research was performed at the Anesthesiology and Resuscitation Departments of the regional healthcare facilities in Astana and Akmola cities. Findings obtained through this research confirm the hypothesis that personality traits in anesthesiologists-resuscitators are factors that facilitate the development of emotional burnout syndrome. Such a discovery necessitates the prevention of personality destructions as well as the establishment of conditions to reduce them in the case of occurrence. Proper arrangement of the work schedule and a favorable working environment allows preventing the emotional burnout among employees.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.098
GPT teacher head0.353
Teacher spread0.254 · 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 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
Published2020
Admission routes1
Has abstractyes

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