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Record W2790106129 · doi:10.1097/jom.0000000000001324

Exploring the Association Between Health Literacy and Psychological Well-Being Among Industry Managers in Germany

2018· article· en· W2790106129 on OpenAlexaboutno aff
Silja Fiedler, Holger Pfaff, Renate Soellner, Timo‐Kolja Pförtner

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2018
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsHealth literacyAssociation (psychology)Quarter (Canadian coin)PsychologyLiteracyPerceptionGerontologyEnvironmental healthMedicinePolitical scienceHealth careGeographyPedagogy

Abstract

fetched live from OpenAlex

OBJECTIVE: Industry managers are typically exposed to high work demands but have received limited attention by research, particularly concerning the issue of health literacy and how this relates to their psychological well-being. The aim of this study was to explore the association between health literacy and psychological well-being among managers in Germany. METHODS: An online survey of a sample of 126 commercial industry managers was conducted. Effects of health literacy on psychological well-being (WHO-5 index) were investigated using path analysis. RESULTS: The findings show a quarter of managers were classified as having poor well-being. Health literacy, namely the facets self-regulation (β = 0.40, P < 0.001), self-perception (β = 0.26, P < 0.001), self-control (β = 0.25, P < 0.01), and proactive approach to health (β = 0.09, P < 0.05), were positively associated with psychological well-being. CONCLUSION: The study indicates that higher health literacy is associated with decreased risk of poor 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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.011
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.084
GPT teacher head0.409
Teacher spread0.324 · 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.

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

Citations31
Published2018
Admission routes1
Has abstractyes

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