Hospital digitalization in the era of industry 4.0 based on GHRM and service quality
Bibliographic record
Abstract
Health services, especially hospitals, are facing significant changes in the industrial era 4.0. Hospital digitization is responsible for building green hospitals and improving service quality in the era of modern technology. Green Human Resources Management (GHRM) is becoming known as the HRM concept that encourages employee commitment and environmental aspects. GHRM is an HR management system suitable for organizations, including hospitals that have a significant environmental impact. Hospitals are very dependent on the quality of service provided to their customers. GHRM as a management model in hospitals is very important to encourage hospital digitization and service quality. This research was conducted to answer the gap in hospital research in seeing the effect of GHRM on service quality in digitizing hospitals in the industrial era 4.0. GHRM and Service Quality is used in the healthcare industry to test its application in hospitals in Indonesia. They are using the SEM PLS method, a variant-based structural equation analysis that focuses on predictive models, to look for predictive linear relationships between variables to process data for 1004 respondents from 19 State-Owned Enterprises' hospitals throughout Indonesia. This research can an-swer research gaps related to the effect of GHRM on Service Quality and Hospital Digitalization in Indonesia. Based on Hospital Digitalization, GHRM affects Service Quality, which is closely related to environmental problems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".