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Record W4400289862 · doi:10.1097/jom.0000000000003182

Costs of Presenteeism and Absenteeism Associated With Psychological Distress Among Male and Female Older Workers

2024· article· en· W4400289862 on OpenAlexaffabout
Mahée Gilbert‐Ouimet, Hélène Sultan‐Taïeb, Karine Aubé, Léonie Matteau, Xavier Trudel, Chantal Brisson, Jason R. Guertin

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2024
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversité LavalUniversité du Québec à RimouskiThe Quebec Population Health Research NetworkUniversité du Québec à Montréal
Fundersnot available
KeywordsPresenteeismAbsenteeismMedicineDemographyDistressPsychological distressProductivityCross-sectional studyGerontologyClinical psychologyPsychiatryPsychologyMental healthSocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: We estimated, for women and men (1) the associations between psychological distress and the prevalences of presenteeism and absenteeism, (2) average hours lost annually per person, and (3) costs from the employers' economic perspective. METHODS: Participants were 1292 Canadian white-collar workers. Psychological distress, presenteeism, and absenteeism were assessed with validated questionnaires. The average annual hours of productivity loss and related costs were estimated using generalized linear models with a negative binomial distribution and a log link. RESULTS: High psychological distress in women and men was associated with presenteeism costs ($6944 and $8432) and absenteeism costs ($2337 and $2796 per person). The association between psychological distress and annual hours lost to presenteeism was twice stronger for men than women. CONCLUSIONS: Productivity losses associated with psychological distress are high in women and men older workers.

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 score0.225
Threshold uncertainty score0.447

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.371
Teacher spread0.343 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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