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Record W1995260925 · doi:10.1258/095148407782219067

Heavy physician workloads: impact on physician attitudes and outcomes

2007· article· en· W1995260925 on OpenAlexaff
Eric S. Williams, Kent V. Rondeau, Qian Xiao, Louis Hugo Francescutti

Bibliographic record

VenueHealth Services Management Research · 2007
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAbsenteeismWorkloadStructural equation modelingStressorHealth careQuality (philosophy)PsychologyJob satisfactionNursingMedicineSocial psychologyClinical psychologyManagementPolitical scienceComputer science

Abstract

fetched live from OpenAlex

The intensity of physician workload has been increasing with the well-documented changes in the financing, organization and delivery of care. It is possible that these stressors have reached a point where they pose a serious policy issue for the entire healthcare system through their diminution of physician's ability to effectively interact with patients as they are burned out, stressed and dissatisfied. This policy question is framed in a conceptual model linking workloads with five key outcomes (patient care quality, individual performance, absenteeism, turnover and organizational performance) mediated by physician stress and satisfaction. This model showed a good fit to the data in a structural equation analysis. Ten of the 12 hypothesized pathways between variables were significant and supported the mediating role of stress and satisfaction. These results suggest that workloads, stress and satisfaction have significant and material impacts on patient care quality, individual performance, absenteeism, turnover and organizational performance. Implications of these results and directions for future research 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)0.009
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.100
GPT teacher head0.549
Teacher spread0.449 · 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

Citations50
Published2007
Admission routes1
Has abstractyes

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