The importance of trustworthiness: lessons from the COVID-19 pandemic
Bibliographic record
Abstract
In a time of myriad threats to the health and well-being of children and their families, pediatricians play a critical role in advancing scientific discoveries, communicating findings to improve public understanding, and integrating evidence into policy and clinical practice. As outlined in the Public Health Code of Ethics, “the effectiveness of public health policies, practices, and actions depends upon public trust gained through decisions based on the highest ethical, scientific, and professional standards.” 1 Multiple urgent issues requiring evidence and advocacy in pediatrics include: (1) inadequate access to primary and subspecialty care, (2) the ongoing coronavirus (COVID-19) pandemic and its associated disruption in education and social services, (3) vaccine confidence, (4) immigration (caring for children and families entering the United States), (5) climate change, (6) environmental toxins, (7) gun violence, and (8) the behavioral and mental health crisis. All of these threats are compounded by racism, social injustice, and inequities in our society and health care system. Moreover, there is a mounting imperative to protect medical science and its integrity in the age of social media and widespread misinformation. We must demonstrate trustworthiness as a requisite condition to foster trust.
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 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 | 0.046 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".