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Record W3158120917 · doi:10.1111/joop.12345

Mindfulness and positive activities at work: Intervention effects on motivation‐related constructs, sleep quality, and fatigue

2021· article· en· W3158120917 on OpenAlexaff
Alexandra Michel, Clarissa Groß, Annekatrin Hoppe, M. Gloria González‐Morales, Anna Steidle, Deirdre O’Shea

Bibliographic record

VenueJournal of Occupational and Organizational Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMindfulnessPsychologyIntervention (counseling)Work engagementPositive psychologyApplied psychologySleep qualityWell-beingQuality (philosophy)Work (physics)Clinical psychologySocial psychologyPsychotherapistPsychiatryCognition

Abstract

fetched live from OpenAlex

Positive psychology research is increasingly being transferred to organizational contexts, and organizations are increasingly striving for healthier and more motivated employees. In this study, a three‐week self‐instructed online intervention which combines positive activities and mindfulness was developed and evaluated using a randomized‐controlled group design with employees. All exercises could be easily integrated into the daily working routine. The intervention is based on broaden‐and‐build theory, the two‐component model of mindfulness and the positive‐activity model. Results indicate that the intervention is effective in increasing work engagement, hope and sleep quality as well as in reducing fatigue. Practical implications for human resource departments and corporate health management are discussed. Practitioner points A three‐week mindfulness intervention can increase work engagement, hope, sleep quality, and reduce fatigue. Such activities can easily be integrated into the workday and thus, represent a realistic way for employees to improve motivation and reduce health impairment. ​

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.046
GPT teacher head0.375
Teacher spread0.329 · 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.

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

Citations50
Published2021
Admission routes1
Has abstractyes

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