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Record W4401032088 · doi:10.1177/00221856241265273

Ironclad work overload: Prevention of psychosocial hazards among union counsellors in Quebec

2024· article· en· W4401032088 on OpenAlexaffabout
Mélanie Lefrançois, Mélanie Trottier

Bibliographic record

VenueJournal of Industrial Relations · 2024
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPsychosocialWorkloadPsychologyWork (physics)Interpersonal communicationApplied psychologyNursingSocial psychologyClinical psychologyMedicineEngineeringManagementPsychiatryEconomics

Abstract

fetched live from OpenAlex

The role of labour union staffers-as-workers, crucial to the functioning of a union, involves a growing number of increasingly complex demands and requirements. A Quebec union concerned about the health of its members, union counsellors employed by another union, commissioned a study of a prominent psychosocial risk factor: work overload. A case study based on a mixed exploratory participatory design (qual→QUAN; 25 semi-structured interviews, 82 questionnaires) identified individual, interpersonal and organisational determinants, consequences, strategies and possible solutions for the prevention of work overload. The study specifies the workload associated with certain tasks, which are perceived as being more demanding both quantitatively and emotionally. High emotional exhaustion and poor work-life balance underline the urgency of preventing overload. The results point to solutions that mitigate collateral impacts and adopt approaches differentiated by sex/gender, career stage and work-life situation. These reflections on the prevention of a type of psychosocial hazard in trade union action emphasise the importance of understanding the components of workload, and may apply to other professional jobs involving care functions and the management of complex cases.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.392
Teacher spread0.349 · 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.

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

Citations2
Published2024
Admission routes2
Has abstractyes

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