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Record W3196862756 · doi:10.53379/cjcd.2021.70

Adaptability and Workplace Subjective Well-Being: The Effects of Meaning and Purpose on Young Workers in The Workpalce

2021· article· en· W3196862756 on OpenAlexaffvenue
Harry Nejad, Fara Nejad, Tara Farahani

Bibliographic record

VenueCanadian Journal of Career Development · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of TorontoAthabasca University
Fundersnot available
KeywordsAdaptabilityMeaning (existential)PsychologyConstruct (python library)Job satisfactionWorkforceWork (physics)Social psychologyPersonalityWell-beingApplied psychologyComputer scienceEngineeringManagementPolitical science

Abstract

fetched live from OpenAlex

Adaptability is described as the apt mental, behavioural, and/or emotional modifications individuals make to deal with change, challenges, and uncertainty. The present paper builds on the recently developed measurement work of the adaptability construct, investigates the relationship between adaptability and meaning and purpose (a well-being factor) and the role of adaptability in predicting workplace subjective well-being (work engagement, job satisfaction, and handling work stress) relevant to the young workforce. The adaptability study concluded that implicit theories and personality significantly projected adaptability. Further, adaptability is shown as the predictor of well-being (including meaning and purpose) after accounting for the effects of presage factors. These results presume implications for executives and practitioners pursuing to identify and address young workers’ approaches to their challenging and adverse workplace demands, and how meaning and purpose may assist these workers in better adjustment and engagement in their workplace.

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)0.003
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.247
Teacher spread0.232 · 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

Citations11
Published2021
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Career DevelopmentSame topicPsychological Well-being and Life SatisfactionFrench-language works237,207