Regulating in the public interest: Lessons learned during the COVID-19 pandemic
Bibliographic record
Abstract
This article has three aims. First, to reflect on how conceptualizations of the public interest may have shifted due to COVID-19. Second, to focus on the implications of regulatory responses for the health workforce and corresponding lessons as health leaders and systems transition from pandemic response to pandemic recovery. Third, to identify how these lessons lead to potential directions for future research, connecting regulation in a whole-of-systems approach to health system safety and health workforce capacity and sustainability. Pandemic regulatory responses highlighted both strengths and limitations of regulatory structures and frameworks. The COVID-19 pandemic may have introduced new considerations around regulating in the public interest, particularly as the impact of regulatory responses on the health workforce continues to be examined. Clearly articulating practitioner practice parameters, reducing barriers to practice, and working collaboratively with stakeholders were primary aspects of regulators' pandemic responses that impacted the health workforce.
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 | 0.075 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.018 | 0.029 |
| Insufficient payload (model declined to judge) | 0.004 | 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".