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Record W1892734234 · doi:10.1002/job.784

Making a significant difference with burnout interventions: Researcher and practitioner collaboration

2011· article· en· W1892734234 on OpenAlexaff
Christina Maslach, Michael P. Leiter, Susan E. Jackson

Bibliographic record

VenueJournal of Organizational Behavior · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsAcadia University
Fundersnot available
KeywordsBurnoutPsychological interventionPsychologyUnit (ring theory)Point (geometry)Applied psychologyMedical educationKnowledge managementSocial psychologyMedicineClinical psychologyComputer scienceMathematics educationPsychiatryMathematics

Abstract

fetched live from OpenAlex

Summary Burnout research over the past 30 years has yielded both knowledge and tools to apply to interventions at unit and organizational levels. Examples of innovative partnerships between researchers and practitioners point to the importance of multi‐level approaches in generating relevant and effective solutions to the burnout problem. Copyright © 2011 John Wiley & Sons, Ltd.

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)0.414
metaresearch head score (Gemma)0.538
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.414
Threshold uncertainty score0.722

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4140.538
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0060.004
Science and technology studies0.0070.006
Scholarly communication0.0170.017
Open science0.0060.017
Research integrity0.0150.019
Insufficient payload (model declined to judge)0.0060.002

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 head0.215
GPT teacher head0.480
Teacher spread0.266 · 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.

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

Citations229
Published2011
Admission routes1
Has abstractyes

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