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Tenderness and Steadiness: Relating Job and Interpersonal Demands and Resources With Burnout and Physical Symptoms of Stress in Canadian Physicians

2010· article· en· W1558883833 on OpenAlexaffabout
Raymond T. Lee, Brenda Lovell, Céleste M. Brotheridge

Bibliographic record

VenueJournal of Applied Social Psychology · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversité du Québec à MontréalUniversity of Manitoba
Fundersnot available
KeywordsEmotional exhaustionDepersonalizationPsychologyBurnoutInterpersonal communicationWorkloadSocial psychologyJob satisfactionEmotional laborAutonomyStressorOccupational stressClinical psychologyManagement

Abstract

fetched live from OpenAlex

This study examined the extent to which job and interpersonal demands and resources are associated with burnout and physical symptoms of stress among Canadian physicians. Using the job demands‐resources (JD‐R) model, we predicted that demands would be more strongly related to emotional exhaustion and physical symptoms, whereas resources would be more strongly related to personal accomplishment and decreased depersonalization. The findings reveal that communication skills and emotional labor contributed to the explained variances beyond workload and work–life conflict (as job demands), as well as autonomy, predictability, and understanding (as job resources). The predictors were differentially associated with the outcome variables in a manner that is consistent with the JD‐R model. Implications for physician well‐being and improved patient outcomes are discussed.

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)0.001
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 score0.103
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.020
GPT teacher head0.386
Teacher spread0.366 · 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

Citations78
Published2010
Admission routes2
Has abstractyes

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