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Record W4387603219 · doi:10.21926/obm.icm.2304042

Coping with Burnout in the Healthcare Field

2023· article· en· W4387603219 on OpenAlexaff
Ami Rokach, Karishma Patel

Bibliographic record

VenueOBM Integrative and Complementary Medicine · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsBurnoutHealth careLonelinessCompassion fatiguePsychologyCoping (psychology)NursingPsychological resilienceDistressMental healthMedicineClinical psychologyPsychotherapist

Abstract

fetched live from OpenAlex

The healthcare industry is the largest and fastest growing industry in the world; however, being a part of the healthcare system involves being at an increased risk of experiencing mental health problems, loneliness, stress, and increased susceptibility to experiencing compassion fatigue related to the emotional wear associated with providing patient-centered care. Healthcare workers include, but are not limited, to physicians, nurses, allied health professionals, and psychologists. Often, healthcare workers place the needs and wellbeing of patients before their own. This article aims to highlight the occupational hazards of working in the healthcare field, the physical and emotional isolation associated with clinical practice, managing distressing behaviors by patients, and reviewing the systemic barriers influencing the development and management of moral distress. We further aim to bring attention to the need for healthcare professionals to place self-care at the forefront of their therapeutic repertoire through various individualized strategies, through the importance of building moral resilience, and the shift towards improving workplace spirituality. Practicing self-care can address the consequences of neglecting one’s own wellbeing, positively impact the ability to provide better quality patient care, and benefits relationships with patients, loved ones, and of most importantly with oneself.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.630
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.114
GPT teacher head0.477
Teacher spread0.363 · 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 designQualitative
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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