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Record W4378951561 · doi:10.1093/workar/waad016

Harnessing the Potential of Older Workers Through Relationships at Work: Social Support, Feedback, and Performance

2023· article· en· W4378951561 on OpenAlexafffund
Tatiana Marques, Sara Ramos, David Patient, D. Ramona Bobocel

Bibliographic record

VenueWork Aging and Retirement · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Waterloo
FundersFundação para a Ciência e a TecnologiaSocial Sciences and Humanities Research Council of Canada
KeywordsSocioemotional selectivity theoryModerationWorkforceAging in the American workforcePsychologyWork (physics)Social supportSocial workSocial psychologyDevelopmental psychologyEngineeringEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Abstract With the aging of the global workforce, it is crucial to deepen our understanding of how to keep older workers healthy, motivated, and productive. In this research, we integrate job design with socioemotional selectivity theory to propose that social job characteristics relate to employee performance differently for older and younger workers. Specifically, in a 3-wave survey (N = 454), we tested employee age as a moderator of the relationships between receiving social support and feedback at work, and performance, as well as giving social support and feedback at work, and performance. The results showed that, in general, both receiving and giving social support and feedback are associated more strongly with the performance of older than younger workers. The findings provide important theoretical implications for the study of aging and work; they also offer practical applications for creating workplaces in which older workers can reap the benefits of social relationships to remain productive.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.245
Teacher spread0.215 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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