Avoiding the Banality of Evil in Times of COVID-19: Thinking Differently with a Biopsychosocial Perspective for Future Health and Social Policies Development
Bibliographic record
Abstract
The COVID-19 pandemic provides the opportunity to re-think health policies and health systems approaches by the adoption of a biopsychosocial perspective, thus acting on environmental factors so as to increase facilitators and diminish barriers. Specifically, vulnerable people should not face discrimination because of their vulnerability in the allocation of care or life-sustaining treatments. Adoption of biopsychosocial model helps to identify key elements where to act to diminish effects of the pandemics. The pandemic showed us that barriers in health care organization affect mostly those that are vulnerable and can suffer discrimination not because of severity of diseases but just because of their vulnerability, be this age or disability and this can be avoided by biopsychosocial planning in health and social policies. It is possible to avoid the banality of evil, intended as lack of thinking on what we do when we do, by using the emergence of the emergency of COVID-19 as a Trojan horse to achieve some of the sustainable development goals such as universal health coverage and equity in access, thus acting on environmental factors is the key for global health improvement.
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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 | 0.039 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.086 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.012 | 0.034 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".