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Record W2323057216 · doi:10.1037/a0038731

Retirees’ motivational orientations and bridge employment: Testing the moderating role of gender.

2015· article· en· W2323057216 on OpenAlexaff
Yujie Zhan, Mo Wang, Junqi Shi

Bibliographic record

VenueJournal of Applied Psychology · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsWilfrid Laurier University
FundersNational Natural Science Foundation of China
KeywordsBridge (graph theory)PsychologySocial psychologyLabour economicsPolitical scienceEconomicsMedicine

Abstract

fetched live from OpenAlex

Bridge employment refers to the labor force participation after people retire from career jobs. It is becoming a prevalent phenomenon for retirees transitioning from employment to complete work withdrawal. Building on existing literature on retirement transition and older adults' work motivation, the present study examined the effects of 3 motivational orientations (i.e., status striving, communion striving, and generativity striving) in relating to retirees' bridge employment participation (i.e., bridge employment status and bridge employment work hours). This study also applied the social gender role theory to examine the effect of gender in moderating the effects of motivational orientations. Data from 507 Chinese retirees in Beijing revealed that communion striving and generativity striving were positively related to bridge employment participation. Further, gender moderated the effect of status striving such that status striving was positively related to bridge employment participation for male retirees but not for female retirees. In addition, exploratory analysis was conducted to examine the effects of the same set of motivational orientations on postretirement volunteering activities. Results showed that status striving was negatively related to volunteering after retirement. The findings are discussed in terms of their theoretical implications for the bridge employment literature and practical implications for recruiting and retaining older workers.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.218

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 studies0.0000.000
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 head0.370
GPT teacher head0.462
Teacher spread0.091 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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