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Record W4362014914 · doi:10.3934/medsci.2023006

Healthcare workers: diminishing burnout symptoms through self-care

2023· article· en· W4362014914 on OpenAlexaff
Ami Rokach

Bibliographic record

VenueAIMS Medical Science · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsYork University
Fundersnot available
KeywordsBurnoutLonelinessHealth careActive listeningIsolation (microbiology)NursingResistance (ecology)TollPsychologyMedicinePsychotherapistPolitical scienceClinical psychology

Abstract

fetched live from OpenAlex

<abstract> <p>Being part of the health care system involves facing stress, loneliness and the emotional toll of assisting, listening and caring for patients who come into the office or hospital, and seek or even demand assistance. Healthcare workers, like physicians, nurses and therapists, are trained, on assisting and healing others. They are less effective in taking care of themselves. This article, which aims to heighten clinicians' awareness of the need for self-care, especially now in the post-pandemic era, addresses the demanding nature of medicine and health work, and the resistance that clinicians commonly display in the face of suggestions that they engage in self-care. The consequences of neglecting to care for oneself are delineated. The demanding nature of medicine is reviewed, along with the loneliness and isolation felt by clinicians particularly those in private practice, the professional hazards faced by those caring for others, and the ways that are available to them (should they decide to care for themselves) for the benefit of their clients, their families and, obviously, themselves.</p> </abstract>

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0050.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.069
GPT teacher head0.463
Teacher spread0.394 · 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; both teacher heads agree on what is shown here.

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
Published2023
Admission routes1
Has abstractyes

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