Green Building, Green Innovation and Green HRM: Determinants of Green Hospital Implementation at West Pasaman Regional General Hospital
Bibliographic record
Abstract
This study aims to determine the effect of various determinants consisting of green building, green innovation and green human resource management (HRM) on the green hospital at Pasaman Barat Hospital.The population in this study were 473 employees of Pasaman Barat Hospital and a sample of 83 employees.The research method used is a survey method and data analysis for the inner model, outer model and hypothesis using SEM PLS.The findings of this study are that green HRM is the main factor driving the implementation of green hospitals in Pasaman Barat Hospital, besides that it is also influenced by the contribution of green innovation.The conclusion of this study is that employees of West Pasaman Hospital must improve green HRM and green innovation to encourage a green hospital at West Pasaman Hospital.This study recommends that future researchers combine questionnaire and interview data as data collection techniques so that the research results are more specific.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".