Eco-Responsible Awareness Raising interventions To reduce the Use of Long-Acting MDIs and Carbon Footprint in a University Hospital Center (EARTH)
Bibliographic record
Abstract
BACKGROUND: There are very limited data studying strategies designed to enhance environmentally conscious MDI prescribing habits. This study aimed to evaluate the impact of a multicomponent strategy promoting more environmentally responsible inhaler prescribing practices on the use of long-acting MDIs. OBJECTIVES: What is the impact of a multicomponent eco-responsible inhaled medication prescribing awareness program on the prescription rate of long-acting MDIs in hospitalized patients? METHODS: This interrupted time series was conducted by retrospectively collecting long-acting inhaler metrics from digitalized records before and after awareness raising interventions. The primary outcome was to assess the impact of the multicomponent awareness campaign on the proportion of long-acting MDIs relative to the total number of long-acting inhalers prescribed on all in-patient hospital wards. The secondary endpoint was to evaluate the difference in proportion of long-acting MDI prescriptions between hospital admission and discharge in selected wards. RESULTS: = 0.319). There was no significant difference in the level or slope between admission and discharge for the secondary outcome. CONCLUSION AND RELEVANCE: A 20.1% reduction in the prescription rate of MDIs could be observed following awareness interventions focused on eco-responsible inhaler prescribing in a hospital setting. This is explained mainly by an initial decrease in the number of prescriptions postintervention and that change remained stable over time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".