How does social isolation in a context of dirty work increase emotional exhaustion and inhibit work engagement? A process model
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the consequences of experiencing social isolation in a context of dirty work. Relying on an integration of the job demands-resources model (Schaufeli and Bakker, 2004) with the social identity approach (Ashforth and Kreiner, 1999), the paper posits that perceived social isolation prevents the development of defense mechanisms that could counter the occupational stigma, and thus tends to increase perceptions of stigmatization, and to decrease perceptions of the prosocial impact of their work. Through these two perceptions, perceived social isolation indirectly affects emotional exhaustion and work engagement. Design/methodology/approach Research hypotheses are tested among a sample of 195 workers in the commercial cleaning industry who execute physically tainted tasks. Findings Results support the research model. Perceived prosocial impact mediates the negative relationship between perceived social isolation and work engagement, and perceived stigmatization mediates the positive relationship between perceived social isolation and emotional exhaustion. Research limitations/implications This research contributes to the dirty work literature by empirically examining one of its implicit assumptions, namely, that social isolation prevents the development of coping strategies. It also contributes to the literature on well-being and work engagement by demonstrating how they are affected by the social context of work. Originality/value The present paper is the first to study the specific challenges of social isolation in dirty work occupations and its consequences.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".