Investigating occupational disidentification: a resource loss perspective
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the concept of occupational disidentification through the lens of conservation of resources (COR) theory (Hobfoll, 1989, 1998). Occupational disidentification is conceptualized as a coping strategy, or an investment of resources to cope with poor perceived prestige of the occupation, which represents a threat to an individual’s resource: one’s self-esteem. However, occupational disidentification, as an avoidance coping strategy, generates a loss of cognitive and emotional resources leading to emotional exhaustion and, in turn, departure from the organization. Design/methodology/approach The research hypotheses are tested among two samples of employees working in health and social services (Study 1, N=544), and in home care services (Study 2, N=113). Measures of employees’ attitudes were collected at the same time, and turnover was collected 18 months (Study 1) and 12 months (Study 2) later. Findings Research hypotheses are all supported. Occupational disidentification partially mediates the occupational prestige-emotional exhaustion relationship, and emotional exhaustion partially mediates the occupational disidentification-turnover intention relationship. Perceived organizational support moderates the negative relationship between perceived occupational prestige and occupational disidentification. Originality/value The main contribution of this study is the conceptualization of occupational disidentification within the theoretical framework of COR. In that vein, the study provides: a deeper understanding of the mechanisms explaining and buffering occupational disidentification, and empirical evidence of the key role of emotional exhaustion to explain the consequences of occupational disidentification.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".