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Enregistrement W4296736564 · doi:10.31265/usps.171

Alcohol-related Problems and Sick Leave: Do Attitudes towards Drinking matter?

2022· dissertation· en· W4296736564 sur OpenAlexaboutno aff
Neda Hashemi

Notice bibliographique

Revuenon disponible
Typedissertation
Langueen
DomaineMedicine
ThématiqueSubstance Abuse Treatment and Outcomes
Établissements canadiensnon disponible
Organismes subventionnairesSouthwest Center for Occupational and Environmental HealthCenters for Disease Control and PreventionNorges ForskningsrådHelsedirektoratetUniversitetet i Stavanger
Mots-clésSick leaveContext (archaeology)Psychological interventionAlcohol consumptionPsychologyEnvironmental healthConsumption (sociology)Social psychologyAlcoholMedicineSociologyGeographyPsychiatry

Résumé

récupéré en direct d'OpenAlex

Background: Drinking alcohol is integrated into people’s social- and work lives. Drinking attitudes and norms stand out as significant predictors of drinking alcohol but few studies have been focused on working populations. Existing norms and attitudes toward alcohol, nature of work, sociocultural context, and workplace culture can form different drinking patterns and subsequently lead to a range of consequences for the individual who drinks, surroundings people, and society as a whole. Earlier studies have revealed that drinking alcohol increases the risk of sick leave among employees. However, there is a lack in exploring subgroups including measurement groupings and type of data. Moreover, the majority of prior studies focused on individual determinants and had less attention on group-level determinants. To better understand the relationship between alcohol behavior and sick leave, there is a need to explore the determinants at both the individual and group levels while considering employees within their work units and organizations. Aims: The overall aim of this thesis was to obtain new knowledge and a deeper understanding of the relationships between alcohol consumption and sick leave (Papers I and III), and how drinking attitudes might have a role in this relationship (Papers II and III). Materials and methods: In this thesis, data from the national WIRUS project (Workplace Interventions preventing Risky alcohol Use and Sick leave) was used. The relationship between alcohol consumption and sickness absence was explored by reviewing previously published literature and was analyzed descriptively (based on type of design, direction of associations, and type of measurement) and using meta- analysis (Paper I). Six databases were searched, and observational and experimental studies from 1980 to 2020 that reported the results of the association between alcohol consumption and sickness absence in the working population were included. Newcastle-Ottawa Scale was applied to assess the quality of each association test. The status of drinking attitudes, as well as the association between drinking attitudes and alcohol-related problems, were examined in a cross-sectional study of 4,094 employees in 19 Norwegian companies (Paper II). Drinking attitudes were assessed using the Drinking Norms Scale, and the Alcohol Use Disorders Identification Test scale was used to assess any alcohol-related problems. The data were analyzed using multiple logistic regression. Paper III, by considering the organizational structure of the working units, explored whether alcohol-related individual differences (drinking attitudes and alcohol-related problems) can predict one-day, short-term, long-term, and overall company-registered sick leave days. The data from the WIRUS-screening study were linked to company-registered sick leave data for 2,560 employees from 95 different work units. Three- level (employee, work unit, and company) negative binomial regression models were used to examine the association between alcohol-related individual differences and sick leave. Results: In Paper I, fifty-nine studies (58% longitudinal) were included in the systematic review. The systematic review supported the association between alcohol consumption and sickness absence, revealing that sickness absence was more than two times higher among risky drinking employees than among low-risk drinking employees. The increased risk for sickness absence was more likely to be found in cross- sectional studies, studies using self-reported absence data, and those reporting short-term sickness absence (Paper I). In Paper II, a higher proportion of employees reported positive (i.e., liberal) drinking attitudes. When compared with employees with negative drinking attitudes, employees with positive drinking attitudes were three times more likely to report alcohol-related problems (Paper II). Moreover, positive drinking attitudes were found to be more frequent in men than in women. However, the association between drinking attitudes and alcohol-related problems was noticeably stronger for women than for men (Paper II). A high variation in sick leave across work units and companies was found in the sample of Norwegian employees (Paper III). However, alcohol-related problems and drinking attitudes showed no association with higher levels of sick leave in work units within companies (Paper III). Conclusions: This thesis supports earlier evidence on the association between alcohol and sick leave in general and suggests that some specific types of measurement groupings and types of data may produce large effects in different ways. Although there was a lack of association between alcohol-related individual differences and sick leave among a sample of Norwegian employees, this thesis suggests the importance of between company-level differences on sick leave over within company differences. Therefore, further research is warranted to explore whether other unmeasured factors and/or specific company policies and practices can explain these differences. Moreover, the thesis suggests that drinking attitudes are associated with alcohol-related problems. To facilitate early health promotion programs that target alcohol problems, employees’ drinking attitudes may be assessed alongside actual alcohol consumption. These assessments might need to be gender-specific.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,032
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,055

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0100,032
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,007
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0030,001
Science ouverte0,0010,001
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,025
Tête enseignante GPT0,309
Écart entre enseignants0,284 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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