Motivators for medical staff with a high gap in healthcare efficiency: Comparative research from Poland and Ukraine
Bibliographic record
Abstract
INTRODUCTION: This article examines different motivators for medical staff in countries with a high gap in healthcare efficiency by comparing them in two healthcare systems-Polish (ie efficient) and Ukrainian (ie inefficient). METHOD: This survey-based study applies a six-stage conceptual framework to two Polish and two Ukrainian hospitals as well as medical faculties of one university from each country. Following ethical approval, data were collected in the first quarter of 2019, using the 'Evaluation of motivators questionnaire for medical staff'. FINDINGS: Medical staff perceived their working conditions in the inefficient healthcare system much worse than in the efficient system; however, they generally had a more optimistic outlook. Medical staff in efficient and inefficient healthcare systems has different motivational targets, including sizable differences from profession, gender, and age. These factors play an important role in developing a high-performance healthcare system. Results are illustrated in terms of motivators for medical staff. CONCLUSION: Optimising a healthcare system requires useful reform of enablers, especially in countries with inefficient systems, including policymaking and regulatory action. Best practices must incorporate all stakeholders interested in high healthcare performance-usage of suitable practices from abroad can act as an important resource.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".