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Record W3132817067 · doi:10.5430/ijhe.v10n3p268

Systematic Review of the Job Demands and Resources of Academic Staff within Higher Education Institutions

2021· article· en· W3132817067 on OpenAlexvenueno aff
Mineshree Naidoo-Chetty, Marieta du Plessis

Bibliographic record

VenueInternational Journal of Higher Education · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsCLARITYJob analysisHigher educationWorkloadJob designWork (physics)Job securityEmpirical researchBusinessHuman resourcesPublic relationsOrder (exchange)Job performanceKnowledge managementPsychologyJob satisfactionPolitical scienceManagementEconomicsEconomic growthComputer scienceEngineeringSocial psychology

Abstract

fetched live from OpenAlex

The Higher Education sector has been through an array of changes, such as globalisation, massification, lack of job security, decolonisation and a number of technological advancements. These changes have impacted academic workload and have increased work pressure with resultant effects on family and work life balance. A review of the existing literature indicates a lack of clarity when it comes to the job demands and job resources inherent to the academic occupation. In order to determine the job demands and job resources of academics, a systematic review of empirical literature is warranted. This paper systematically reviewed empirical research published from 2014 to 2019 investigating job demands and resources based on the job demands-resources model in the higher education environment. Six articles were identified that met the criteria for inclusion. Thus, a list of quantitative, qualitative and organisational job demands as well as organisational and personal resources specific to the academic environment were identified. This will allow Higher Education Institutions to provide targeted development of job resources and mitigation of job demands for their academic employees and enable the development of specific interventions.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.625
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.310
Teacher spread0.292 · 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

Citations45
Published2021
Admission routes1
Has abstractyes

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