Falsification of Type at Work: Assessment of Prevalence and Investigation of Predictors
Bibliographic record
Abstract
BACKGROUND: Occupational-stress, job-satisfaction and poor health outcomes are closely related and strongly pertain to individuals' mental health and physiological well-being. Falsification of Type is a growing term in the field of organisational psychology that measures occupational stress when working in a job that does not match one's, natural leader. AIM: The present work aims at determining the prevalence of falsification of type and associated socio-demographic and work-related factors. METHODS: The study sample consists of 150 researchers working at the National Research Centre of Egypt. Participants were asked to complete a self-report Falsification of Type Questionnaire, Andrews and Withey scale for Job Satisfaction, in addition to socio-demographic and work-related variables. Statistics included descriptive and comparative analyses. A regression model was built with falsification of the type as the dependent variable. RESULTS: Facilities showed the highest rate of dissatisfaction in the Job Satisfaction Questionnaire. The most prominent manifestations of falsification were fatigue and irritability, and its predictors were the position, interpersonal relationships, facilities and sex according to the regression model. Falsification of type could seriously contribute to occupational stress. Job satisfaction is highly about falsification. CONCLUSION: More research on the Falsification of Type at work is recommended with the greater attention of employers to the importance of the concept of person-job fit.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".