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Record W4296736564 · doi:10.31265/usps.171

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

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

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersSouthwest Center for Occupational and Environmental HealthCenters for Disease Control and PreventionNorges ForskningsrådHelsedirektoratetUniversitetet i Stavanger
KeywordsSick leaveContext (archaeology)Psychological interventionAlcohol consumptionPsychologyEnvironmental healthConsumption (sociology)Social psychologyAlcoholMedicineSociologyGeographyPsychiatry

Abstract

fetched live from 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.

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)0.010
metaresearch head score (Gemma)0.032
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: none
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.007
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.025
GPT teacher head0.309
Teacher spread0.284 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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