Healthcare sobriety and pollution awareness for a green healthcare sector: a Belgian qualitative study
Bibliographic record
Abstract
Abstract The healthcare sector’s contribution to climate change and pollution more broadly is now widely recognised as problematic. Not only does it disrupt the ecosystems and the living environment, but it also paradoxically affects human health. In recent years, an increasing number of countries have committed to reducing the ecological footprint, or in other words, the ‘environmental cost’ of their healthcare system. Achieving a responsible transition toward a greener healthcare sector requires considering the perspectives and potential roles of various actors and stakeholders within the field. Thus far, very few studies have investigated the perspectives of (potential) healthcare consumers on challenges, ethical issues and social tensions that could arise during the transition to a greener healthcare system. To address this gap, we carried out five group interviews, exploring the views of 28 participants on climate change, healthcare pollution, and their sense of engagement in reducing healthcare pollution. Data were collected, coded and analysed using an inductive process. While most participants perceived climate change as a serious threat to human health, many were unaware of the healthcare sector’s significant contribution to it. Pharmaceutical waste and plastic pollution were identified as the main problems. Two promising avenues for reducing healthcare pollution emerged from the findings: promoting healthcare sobriety and improving education for actors and stakeholders on the sector’s contribution to global pollution. Participants defined healthcare sobriety through four key elements: adequate care, collective responsibility, ecological finance, and prevention. Regarding education, they underline that it should be done at the right time, by the right person and in an effective manner. Two important barriers to achieving these goals were identified: participants are less willing to accept trade-offs when it is health-related; and ethical concerns were raised about mobilizing vulnerable populations to achieve carbon neutrality in the healthcare sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".