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Record W2761413807 · doi:10.5539/ijps.v9n4p13

The Influence of the Big Five Personality Traits on Burnout in Medical Doctors

2017· article· en· W2761413807 on OpenAlexvenueno aff
Anita Sharma, Neelabh Kashyap

Bibliographic record

VenueInternational Journal of Psychological Studies · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsnot available
Fundersnot available
KeywordsDepersonalizationExtraversion and introversionPsychologyConscientiousnessBig Five personality traitsNeuroticismAgreeablenessVariance (accounting)PersonalityOpenness to experienceExplained variationEmotional exhaustionHierarchical structure of the Big FiveBurnoutClinical psychologySocial psychologyStatistics

Abstract

fetched live from OpenAlex

The objective of present study was to investigate the relationship between the Big Five personality factors and burnout in medical doctors. The sample comprised of 100 doctors (50 males and 50 females). Results of the study revealed that personality factors explained significant amount of variance in both the males’ and females’ sample. In females’ sample, agreeableness explained the maximum variance of 26% (r=-.507**, p<.01) in depersonalization, extraversion explained 12% (r=-.355*, p<.05) of variance in emotional exhaustion, conscientiousness explained 11% (r=-.351*, p<.05) of variance in reduced personal accomplishment and neuroticism explained 9% (r=.098) of variance in reduced personal accomplishment. In males’ sample, extraversion turned out to be the best predictor of emotional exhaustion and explained 11% (r=-.385**, p<.01) of variance in the said variable and openness explained about 10% (r=-.319*, p<.05) of variance in depersonalization. Overall these personality factors have explained 58% of variance in females’ sample and 21% of variance in males’ sample.

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)0.003
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.187
GPT teacher head0.561
Teacher spread0.374 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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