An assessment of performance appraisal satisfaction levels among physicians: Investigation from the healthcare sector in Qatar
Bibliographic record
Abstract
Performance appraisal is an ongoing process between managers and employees.In fact, the fairer the process in designing the performance appraisal, the better the employee satisfaction.However, realizing fairness in performance appraisal process is a tedious task.The main objective of this study is to investigate the relationship between how employees may perceive fairness of performance appraisal system and how this would affect work performance and intention to leave.This investigation is likely to be executed among physicians working in the health sector in Qatar.In order to achieve this objective, a model is framed and investigated where about one hundred physicians respond to a questionnaire which was designed in order to assess the performance appraisal satisfaction.Statistical results show a partial positive relationship between organizational justice (interview, and outcome) and performance appraisal satisfaction.Moreover, partial positive relationship between performance appraisal satisfaction and work performance is statistically proven.Differently, a weak relationship is noticed between intention to leave and perceiving fairness in performance appraisal.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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, 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".