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Record W2497540649 · doi:10.1080/13548506.2016.1210177

Burnout and occupational participation among dentists with teaching responsibilities in universities

2016· article· en· W2497540649 on OpenAlexaboutno aff
Meral Huri̇, Nilsun Bağış, Hakan Eren, Onur Başıbüyük, Sedef Şahın, Mutlu Umaroğlu, Kaan Orhan

Bibliographic record

VenuePsychology Health & Medicine · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsnot available
Fundersnot available
KeywordsDepersonalizationBurnoutEmotional exhaustionOccupational burnoutPsychologyClinical psychologyOccupational therapyOccupational stressMedical educationMedicinePsychiatry

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the levels of burnout and explore the relationships between burnout and occupational participation among dentists with teaching responsibilities. Canadian Occupational Performance Measure (COPM) was used to evaluate occupational participation with questions on demographic information among 155 dentists with teaching responsibilities. Age, gender, years of experience, academic position were the factors affecting level of burnout and occupational participation. Occupational performance score was negatively correlated with emotional exhausment (r = -.731) and depersonalization (r = -.693) while positively correlated with personal accomplishment (r = .611). Occupational satisfaction scores were negatively correlated with emotional exhausment (r = -.631) and depersonalization (r = -.625) while positively correlated with personal accomplishment (r = .614). Occupational participation level can effect burnout among dentists with teaching responsibilities. Further studies with a larger sample are needed to investigate these preliminary results deeply.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.002
Research integrity0.0000.001
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 head0.077
GPT teacher head0.514
Teacher spread0.437 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2016
Admission routes1
Has abstractyes

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