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Record W4402062406 · doi:10.1080/13603108.2024.2392165

The impact of high-performance work practices on employee burnout experience in UK higher education: a professional services perspective

2024· article· en· W4402062406 on OpenAlexfundno aff
Kelli Wolfe

Bibliographic record

VenuePerspectives Policy and Practice in Higher Education · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsnot available
FundersTrent UniversityNottingham Trent University
KeywordsBurnoutPerspective (graphical)PsychologyWork (physics)Professional servicesProfessional developmentApplied psychologyMedical educationBusinessPublic relationsPedagogyMedicinePolitical scienceEngineeringClinical psychologyComputer science

Abstract

fetched live from OpenAlex

Evidence demonstrates work intensity is on the rise and can lead to higher rates of burnout. The UK Higher Education professional services sector has been under-researched in terms of work intensity levels and experience of burnout. This research measured these levels and sought evidence of whether high-performance work practices (HPWPs) moderate the user’s level of burnout. Quantitative data were collected using a cross-sector survey and tested for correlation. The results evidenced a moderate, positive relationship between work intensity and burnout, and a strong, negative relationship between HPWPs and burnout. Drawing on the Job Demands – Resources model (Demerouti et. al. 2001), this research contributes to the understanding of the factors contributing to burnout in the UK Higher Education context. Practical implications include addressing the widespread culture of overwork and employing HPWP suites to moderate burnout. Suggestions for further research provide additional clarity.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.087
GPT teacher head0.537
Teacher spread0.450 · 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 teacher head, 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

Citations8
Published2024
Admission routes1
Has abstractyes

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