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Record W4385275383 · doi:10.21802/artm.2023.2.26.205

PREVALENCE OF BURNOUT SYNDROME IN HEALTHCARE WORKERS IN NORTH AND SOUTH AMERICA, AND ASIA FROM 2018 TO 2022

2023· article· en· W4385275383 on OpenAlexaboutno aff
Вікторія Ботякова

Bibliographic record

VenueArt of Medicine · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsnot available
Fundersnot available
KeywordsBurnoutHealth careHealthcare workerBurnout syndromeMedicineFamily medicineNursingPolitical scienceClinical psychology

Abstract

fetched live from OpenAlex

The article explores issues concerning prevalence of burnout syndrome in healthcare workers in North and South America, and Asia from 2018 to 2022. Thus for this purpose, a great number of scientific sources that are related to the topic of the research were examined. Healthcare professionals face a tremendous strain during the performing of their activities that often may lead to stress and burnout syndrome. In particular, duties of healthcare workers include high responsibility for life and health of a patient, self-discipline, urgent decision-making, empathy, high productivity during extreme conditions, constant psychological and intellectual tension. During the past 30+ years, burnout syndrome was studied by scientists, practitioners, and also by general public all around the world. It should be noted, that nowadays a lot of employees (in particular, healthcare workers) are faced with rapid changes in our modern working life, namely, time pressure, pressure of higher productivity/quality of work, need to learn new skills, increasing demands of adaptation to new types of work, hectic jobs, etc., that in result may cause burnout syndrome. Burnout syndrome of healthcare workers is usually associated with poor quality of medical care and may lead to medical errors, exhaustion, inefficiency, and conflicts. High levels of burnout syndrome among medical professionals of different countries around the world vary from 7,4% to 66%. The issues concerning prevalence of burnout syndrome in healthcare workers in North and South America, and Asia from 2018 to 2022 have not been sufficiently identified and also require more detailed research. Research of scientists that used the Maslach Burnout Inventory (hereinafter – the MBI), and the Copenhagen Burnout Inventory (hereinafter – the CBI) to research the burnout syndrome in healthcare workers of different specialties (including "Family Medicine") were included in this article. Based on the conducted research, the following conclusions can be reached: the research carried out to identify prevalence of burnout syndrome in healthcare workers in North and South America, and Asia from 2018 to 2022 found out the presence of burnout syndrome in healthcare workers ranging from 1,3% to 82,1%. Moreover, prevalence of burnout syndrome in healthcare workers in North and South America varied from 1,3% to 73,5%, whereas in Asia it varied from 5,2% to 82,1%. Factors that associated with burnout in healthcare workers in North and South America, and Asia were examined. The majority of scientific studies on the identification of the prevalence of burnout syndrome in healthcare workers from 2018 to 2022: a) in North and South America have been conducted in Canada, the United States of America, the Federative Republic of Brazil, and the Argentine Republic, etc., whereas b) in Asia have been conducted in China, Japan, India, Pakistan, Iran, Lebanon, Saudi Arabia, Turkey, Oman, Cyprus, Israel, Jordan, and Kazakhstan, etc. Intensivists, physiatrists, resident physicians, oncologists, general surgeons, internal medicine physicians, and emergency medicine physicians are special categories of healthcare workers who are at a high risk of formation of burnout syndrome that may develop due to the specific of professional activity.

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)0.001
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 score0.047
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.045
GPT teacher head0.392
Teacher spread0.346 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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