Avoiding the Banality of Evil in Times of COVID-19: Thinking Differently with a Biopsychosocial Perspective for Future Health and Social Policies Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it