Towards the Development of a Conceptual Framework Adapted to the Specific Needs of Healthcare and Social Services Establishments for a Sustainable Transition of Their Supply Chain
Bibliographic record
Abstract
Abstract The Covid-19 crisis has highlighted the vulnerability of healthcare establishments’ supply chains, highlighting their dependence on a globalised chain that is primarily focused on economic aspects rather than the essential criteria of robustness and sustainability. It is becoming imperative for healthcare establishments to adapt their strategies so as to transcend the current approach focused primarily on efficiency and cost reduction, in favour of an intelligent, integrated supply chain. This must encourage the sharing of information while promoting proactive environmental and social initiatives. This article is the first stage in a research programme designed to provide managers of healthcare institutions with theoretical, methodological and empirical tools to help them in their quest to transition their supply chain towards sustainable development. It also aims to propose reliable and robust measurement methods and tools for determining the economic (minimisation of supply, production, storage and transport costs), environmental (reduction of waste, rationalisation of waste, reduction of GHG emissions, etc.) and social (working conditions and patient satisfaction) benefits arising from the sustainable transition of the supply chain. To do this, we used a narrative literature review. The literature review enabled us to identify nine key elements which, a priori, favour a sustainable supply chain transition in healthcare establishments. We grouped these key elements into major themes, namely the perspective of sustainable development, the prerequisites for a sustainable transition, best practices for a sustainable transition, the performance resulting from the transition, and the learning healthcare systems approach, including the precepts of communicative action. Despite the scarcity of literature on this theme, this study has enabled us to identify key elements, analyse their interactions and highlight existing gaps. These discoveries will guide future research in this field.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".