Beyond the Usual Suspects: Job Desperation as a Driver of Turnover Intention and Job Search Behaviour
Bibliographic record
Abstract
Workers may experience job desperation, characterized by frustration, pressure to quit and readiness to take extreme steps to secure a new job position. We examined the psychometric properties of a French-language version of the Job Desperation Scale by administering a survey to three independent cohorts of French-speaking employees, with a view to assessing the antecedents of job search behaviour and intention to quit. The first (n = 253) and second (n = 184) cohorts, composed of employees from France and Canada respectively, responded to the survey for the exploratory and confirmatory stages of analysis. The third sample (n = 252) of French employees responded both to the survey and to additional measures that typically identify antecedents of job search behaviour and turnover intention. The findings confirm the single-factor structure and robustness of the French-language version. More importantly, they show the substantial and incremental predictive power of job desperation in explaining job search behaviour and turnover intention. These insights help explain job desperation as a critical factor in contemporary labour markets and contribute to the vocational behaviour literature.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".