Competence-based approach to personnel management in healthcare: historical aspects.
Bibliographic record
Abstract
Based on the analysis of publications, historical aspects of the formation and implementation of competency models for personnel management in healthcare are presented. Purpose: based on an analysis of global experience in applying the competency-based approach in personnel management, to assess the degree of implementation of corporate models of personnel competencies in healthcare. Materials and methods. An in-depth study of the literature on the development and application of a competency model in management in medical organizations from a historical perspective was carried out. Methods used: historical, analysis. Results. The introduction of personnel assessment based on soft competencies into healthcare practice in European countries, Canada, and the USA has proven itself to be successful. In Russia, the modern model of managing a medical organization requires a global and dynamic revision. The competency-based approach to personnel management in healthcare has a high level of theoretical development: the methodology of its construction and the regulatory framework. However, the experience of implementing this approach in healthcare, organizing all stages and procedures for working with personnel in conditions of personnel shortages in a specific medical organization, is in its infancy. The implementation of a competency-based approach to personnel management in healthcare has a high level of theoretical development: the methodology of its construction and the regulatory framework. Despite the high level of theoretical development, the modern model of managing a medical organization requires a global and dynamic revision. In Russia, the experience of implementing this approach in healthcare, organizing all stages and procedures for working with personnel in conditions of personnel shortages in a specific medical organization, is in the process of formation. Findings. The methodology for constructing a corporate model of soft competencies of personnel, the algorithm and diagnostic tools for personalized assessment of personnel in a specific medical organization require detailed development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".