The Role of Human Resources Management Towards Healthcare Providers Retention during Covid-19 Pandemic in Egypt
Bibliographic record
Abstract
HRM has a significant role in motivating the employees and ensuring that every employee is satisfied with the HR practices. Healthcare providers have been facing stress and depression especially in Egypt, due to COVID-19. In this country, the healthcare providers are looking for alternatives to achieve satisfaction as COVID-19 has affected their jobs, salaries, incentives, and bonuses. The past studies have focused on assessing the HRM’s role in employee retention and satisfaction during the COVID-19 pandemic in Egypt. The past research discussed the impact of motivation, incentives, and rewards on the employees’ motivation. This study focused on evaluating the role of HRM towards the healthcare providers' retention during COVID-19 in Egypt. This study relied on the quantitative approach for achieving the findings and conclusion. The sample size of the study involved 120 healthcare providers working in different hospitals. The results revealed that intrinsic motivation, rewards, incentives, monetary benefits, and non-monetary benefits have an essential role in maintaining the healthcare providers during COVID-19. It is concluded that HR can play a significant role in retaining key healthcare providers in COVID-19. The elements including intrinsic motivation, incentives, non-monetary, and monetary benefits can play a significant part in retaining healthcare providers. HR departments should focus on releasing salaries on time, providing specific bonuses, and providing incentives to healthcare providers whenever they perform at their best level.
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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 | 0.005 |
| 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 | 0.001 | 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".