Cause-effect chains in S-LCA based on DPSIR framework using Markov healthcare model: an application to “working hours” in Canada
Bibliographic record
Abstract
Abstract Purpose This study has two aims: first, propose the use of the driver-pressure-state-impact-response (DPSIR) framework to expand the normal focus of impact pathways in social life cycle assessment (S-LCA) on endpoint impacts to a systematic analysis to find links between the main sources of social issues and impacts; second, develop a new impact assessment method to quantify the lifetime health and economic outcomes associated with social subcategories, for the first time, using decision analytic models. Methods The DPSIR framework is mapped to the corresponding elements of the S-LCA context in relation to the social subcategories defined in the UNEP/SETAC methodological sheets. Next, a more robust approach is developed for cause-impact chains between social subcategories and impacts on human well-being based on decision-analytic models (decision trees and Markov models) using healthcare approaches and data. Finally, the health and economic consequences associated with social subcategories are quantified by using Quality Adjusted Life Years (QALYs) and costs based on medical literature and healthcare studies. Results and discussion The method was applied to the “working hours” social subcategory in Canada. The cause-effect chain is built using DPSIR framework in relation to the current social issue in Canada of working more than standard hours. Results of the decision analytic model show that working standard hours is more effective and cost-saving than working more than standard hours from the Canadian healthcare perspective. Working standard hours compared to more than standard hours led to an increase of 0.73 QALY and decrease in cost of $6702 per worker. Based on an estimated 2.4 million Canadian workers working more than standard hours, this resulted in a total gain of 1.7 million QALYs and saving of $16 billion overall. Using cost-effectiveness analysis, possible interventions at multiple entry points of the cause-effect chain within DPSIR framework are proposed to reduce the negative health impacts and associated costs of working more than standard hours in Canada. Conclusions Applying the method on other subcategories could help decision-makers establish the cause-effect aspects of the social performance of their product systems using a quantitative systematic analysis from a life cycle perspective. This approach supports corporate decision-makers to quantify social impacts associated with their product supply chains by calculating QALYs and healthcare costs of their socio-economic conditions enabling them to identify possible interventions to improve the social performance.
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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 | 0.009 | 0.001 |
| 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".