Sustainable Consumption of Healthcare: Linking Sustainable Consumption with Sustainable Healthcare and Health Consumer Discourses
Bibliographic record
Abstract
The importance of sustainable consumption has received recent attention in light of the 2013 publication of the United Nations' Post 2015 Development Agenda. Sustainable consumption concerns itself with promoting and maintaining equilibrium between human need and existing resources in order to ensure the longevity and success of tomorrow's generations. Health care is human need and depends on resources. At the same time health clients increasingly want to be in the driver's seat with their health interventions; hence, the concepts of patient-driven healthcare and people driven health research have gained in popularity. We see movements towards a 'quantified self' (where people diagnose themselves), patient-driven healthcare and research models, and health social networks and participatory medicine with an active health technology market that makes consumer personalized medicine possible. Within the sustainable consumption framework the question is which health consumer desires are sustainable. The inclusion of health care and its relationship to sustainable consumption is vital as health care, a finite resource, is essential to good health and sustainable development, but can be negatively impacted by unsustainable consumption patterns. However although one obtains hits in Google or Google Scholar for terms such as "sustainable consumption", "health consumer", sustainable healthcare", "sustainability of healthcare" and "healthcare sustainability" one obtains no hits for the phrase "sustainable consumption of healthcare". In our contribution we posit to bring the sustainable healthcare, health consumer and sustainable consumption discourses together.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.054 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".