NEW TRENDS IN HRM & KNOWLEDGE MANAGEMENT IN THE HEALTH SECTOR BEYOND COVID-19 - A PRACTICAL MODEL
Bibliographic record
Abstract
The world of work and the status of the human being is constantly changing by the influence of new technologies and the contributions of scientists and practitioners of HRM; therefore, the human being is currently considered as a wealth and its value is maximized. Economists have called it (human capital), accountants (human assets), while contemporary managers have called it (knowledge capital). This change has occurred in the perception of HR due to the transformations that affected the work process began with the TIC revolution of the 21st century, which is concentrated on creativity, innovation based on Competencies to achieve total quality and well-being. But globalization, by for-profit companies, has prioritized profit and the values added to the detriment of which weakened the existence of medical organizations and their personnel as revealed by the Covid-19. Currently, political leaders in the democratic world are turning their eyes and considering human strategies that protect human and open the doors of health protection. Strategic and innovative leadership move on to the development of preventive management which concentrates its priority investments on the KM. New HRM trends, knowledge management (KM) and transformational organizations have become a “strategic” investment as an option in institution building and in health organizations. This new approach adopts the method of KM, competency management in HRM in order to achieve a high-level performance of merit characteristics, efficiency and effectiveness, which combine (motivation, constant incentive, qualities of excellence, clear self-orientation, indepth and up-to-date knowledge and competencies, ...). This paper presents the subject of new trends in HRM and KM in the health sector as well as the principles of these trends, European, Canadian and American application models, it offers a practical model aimed at improving the health system after Covid-19, in close relation with the improvement of HRM.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".