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Record W3007913840 · doi:10.2196/14093

Occupational Health Needs and Predicted Well-Being in Office Workers Undergoing Web-Based Health Promotion Training: Cross-Sectional Study

2020· article· en· W3007913840 on OpenAlexaff
Devan Tchir, Michael Szafron

Bibliographic record

VenueJournal of Medical Internet Research · 2020
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversity of Saskatchewan
FundersDeutscher Akademischer Austauschdienst
KeywordsHealth promotionWorkplace health promotionOccupational health nursingHealth educationPsychological interventionMedicineHealth careNursingPsychologyPublic healthMedical education

Abstract

fetched live from OpenAlex

BACKGROUND: Office workers face workplace-related health issues, including stress and back pain, resulting in considerable cost to businesses and health care systems. Workplace health promotion attempts to prevent these health issues, and the internet can be used to deliver workplace health promotion interventions to office workers. Data were provided by Fitbase GmbH, a German company, which specializes in workplace health promotion via the internet (Web-based health). The Web-based health intervention allowed workers to focus on different health categories by using information modules (reading health information) and/or completing practical exercises (guided, interactive health tutorials). OBJECTIVE: This study aimed to identify the extent to which office workers have workplace-related health issues, assess whether office workers who differ in their health focus also differ in their improved well-being, and assess whether completing practical exercises is associated with improved well-being compared with reading information modules. METHODS: Fitbase GmbH collected data for the period of February 2016 to May 2017 from health insurance employees undergoing Web-based health training in Hamburg, Germany. The data consisted of a needs assessment examining health issues faced by office workers, a wellness questionnaire regarding one's perception of the Web-based health intervention, and activity logs of information modules and practical exercises completed. Through logistic regression, we determined associations between improved well-being from Web-based health training and differences in a worker's health focus and a worker's preferred intervention method. RESULTS: Nearly half of the office workers had chronic back pain (1532/3354) and felt tense or irritated (1680/3348). Over four-fifth (645/766) of the office workers indicated that the Web-based health training improved their well-being (P<.001). Office workers who preferred practical exercises compared with information modules had 2.22 times greater odds of reporting improved well-being from the Web-based health intervention (P=.01; 95% CI 1.20-4.11). Office workers with a focus on practical exercises for back health had higher odds of improved well-being compared with other health foci. Office workers focused on practical exercises for back pain had at least two times the odds of having their well-being improved from the Web-based health intervention compared with those focused on stress management (P<.001), mindfulness (P=.02), stress management/mindfulness (P=.005), and eye health (P=.003). No particular health focus was associated with improved well-being for the information modules. CONCLUSIONS: Office workers frequently report having back pain and stress. A focus on Web-based health training via practical exercises and practical exercises for back health predict an improvement in office workers' reported well-being.

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)0.002
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.175
GPT teacher head0.514
Teacher spread0.339 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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