“Knowing the facility first” – Analysing environmentally relevant structures and processes in hospitals: A case study
Bibliographic record
Abstract
The operation of hospitals creates significant environmental burdens due to their large energy and material requirements, in addition to their production of hazardous wastes. Past research has predominantly focused on the role that medical technology and building design have on the environmental impact of hospital operations. In this paper, a holistic framework is developed to analyse hospital operation in order to better understand the processes, employee behaviours, and structures which contribute to the environmental impact of hospital operation. Specific focus is put on the derivation of a simple but effective method, which can also be applied by non-medical and non-specialized consultant personnel in general clinical contexts. Through the use of three empirical pathways, the employee perspective, patient perspective, and building perspective, data was gathered via a case study of the Children’s Hospital (CH), Medical Center, University of Freiburg, Germany. Results revealed linkages between specific employee processes and the consumption of energy and materials, as well as potential pathways for future sustainability relevant monitoring. Characteristics of the hospitals administrative organizational and operational characteristics highlight the difficulties in gathering pertinent data for a complete analysis. Insights, in particular regarding employee behaviours, provide avenues for future research to better understand the implementation of sustainability programs in hospitals.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".