Financial compensation and talent retention in COVID-19 era: The mediating role of career planning
Bibliographic record
Abstract
This research seeks to contribute to the retention Duhok kidney & diseases transplantation center (DKDTC), and health organizations their talent in COVID-19 era. To achieve this objective, we use (IBM SPSS Amoss V.22) to analyze the mediating role of career planning (CP) in the relationship of financial compensation (FC) with talent retention (TR) in DKDTC. The data collected was analyzed through 63 questionnaires, which was distributed to the talents working in DKDTC from May 2020 to March 2021. The researchers reached several conclusions, the most important of which are that CP has a partial mediating role in the relationship between FC and TR. Therefore, this research recommends enhancing the ability of DKDTC and health organizations to TR in a COVID-19 era and they must be relying on FC, and a program that includes clear steps in CP.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".