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Record W1601494469 · doi:10.1002/9781118713860.ch8

Meaningfulness as a Resource to Mitigate Work Stress

2014· other· en· W1601494469 on OpenAlexaff
Sharon Glazer, Małgorzata W. Kożusznik, Jacob H. Meyers, Omar Ganai

Bibliographic record

Venuenot available
Typeother
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStressorPsychologyExistentialismSocial psychologyConservation of resources theoryAffect (linguistics)Resource (disambiguation)Resource dependence theoryClinical psychologyEpistemologyManagement

Abstract

fetched live from OpenAlex

Over the past decade, scholars have returned to existential philosophical roots to understand how meaningfulness in life might affect stressor-strain relationships. As with depression, people who lack meaningfulness in life are more prone to substance abuse, think about committing suicide, feel less in control of their lives, and feel disengaged, depressed, disconnected, alienated, burned out, and lower general mental well-being. This chapter first defines stress and meaningfulness. It then reviews motivational theories (self-determination theory, values theory, conservation of resources (COR) theory, and terror management theory (TMT)) and individual difference constructs (sense of coherence (SOC)) that support the contention that meaningfulness plays an important role in mitigating stress. The chapter also discusses cultural implications of meaningfulness in life on the relationships between stressors and strains, as well as the role of the workplace in providing resources and opportunities for individuals to pursue goals in an effort to create meaningfulness.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.002
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.015
GPT teacher head0.307
Teacher spread0.292 · 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

Citations22
Published2014
Admission routes1
Has abstractyes

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