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Record W2773372231 · doi:10.5539/jms.v7n4p150

Job Stress in Journalism: Interaction between Organisational Support and Job Demands–Resources Model

2017· article· en· W2773372231 on OpenAlexvenueno aff
Imad Al Muala

Bibliographic record

VenueJournal of Management and Sustainability · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsModerationNewspaperTask (project management)PsychologyMultilevel modelJob attitudeJob performanceJob stressJob designStress (linguistics)Applied psychologyQuality (philosophy)Job analysisSocial psychologyPublic relationsJob satisfactionPolitical scienceManagementBusinessComputer scienceAdvertising

Abstract

fetched live from OpenAlex

This study aims to analyse the impact of job demands and job resources on job stress among journalists in Jordan. In addition, the moderation effect of organisational support on such relationship is assessed in this research. A questionnaire survey was conducted among journalists working in daily newspapers in Jordan. This study used multiple and hierarchical regression analyses and determined a significant and positive relationship amongst emotional demands, job insecurity, and task significance on job stress. Additionally, organisational support moderated the relationship between task significance and job stress. Results of study revealed that the organisational support moderates the relationship between task significance and job stress. This finding could challenge journalists, newspaper managements and decision-makers in Jordan. When journalists work on sensitive topics and are in conflict areas, they are in need of additional support from newspaper managements to mitigate high job stress and motivate them to produce quality work.

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)0.003
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.023
GPT teacher head0.324
Teacher spread0.301 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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