Consensus on Prioritisation of Actions for Reducing the Environmental Impact of a Large Tertiary Hospital: Application of the Nominal Group Technique
Bibliographic record
Abstract
Hospitals are the largest greenhouse gas producers within the Australian healthcare sector due to the large amounts of energy, resource utilization, equipment and pharmaceuticals required to deliver care. In order to reduce healthcare emissions, healthcare services must take multiple actions to address the broad range of emissions produced when delivering patient care. The goal of this study was to seek consensus on the priority actions needed to reduce the environmental impact of a tertiary Australian hospital. A nominal group technique was utilized within a multidisciplinary, executive-led environmental sustainability committee to find consensus on the 62 proposed actions to reduce the environmental impact of a tertiary Australian hospital. Thirteen participants joined an online workshop during which an educational presentation was delivered, 62 potential actions were privately ranked according to two domains of 'amenability to change' and 'scale of climate impact' and a moderated group discussion ensued. The group achieved verbal consensus on 16 actions that span staff education, procurement, pharmaceuticals, waste, transport and advocacy on all-electric capital works upgrades. In addition, the individual ratings of potential actions according to each domain were ranked and shared with the group. Despite a large number of actions and varied perspectives within the group, the nominal group technique can be used to focus a hospital leadership group on priority actions to improve environmental sustainability.
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 | 0.109 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".