Multiple Jobholding: An Integrative Systematic Review and Future Research Agenda
Bibliographic record
Abstract
Despite sizable but varying estimates of multiple jobholding (MJH) and decades of research across disciplines (e.g., management, economics, sociology, health and medicine), our understanding of MJH is rather limited. The purpose of this review is to provide a coherent synthesis of the literature on MJH, or working more than one job. Beginning with a discussion of the motivations and demographic predictors that forecast MJH, we note a distinct divide between the research that predicts MJH and the research that examines outcomes, with few studies exploring how motivations might relate to MJH experiences and outcomes. Another significant observation in this review is the inconsistency of findings across and within disciplines regarding whether MJH is depleting or enriching. Using this framework to organize our review, we attempt to reconcile the generally mixed results by presenting research on mechanisms and boundary conditions of MJH to explain how and when multiple jobholders (MJHers) are depleted or enriched. By integrating findings from the literature, we are able to articulate more clearly the paths of depletion and enrichment and discuss how push versus pull-based motivations to hold multiple jobs likely predict these pathways. Finally, we provide a strategic agenda highlighting areas where additional research is urgently needed to equip scholars with practical knowledge on how to help MJHers manage their multiple work roles and how to help organizations manage MJHers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".