Under the spotlight: understanding the role of the Chief Medical Officer in a pandemic
Bibliographic record
Abstract
As the COVID-19 pandemic took hold in 2020, Chief Medical Officers (CMOs) entered the public spotlight like never before. Amidst this increased visibility, the role is deeply contested. Much of the disagreement concerns whether CMOs should act independently of the government: while some argue CMOs should act as independent voices who work to shape government policy to protect public health, others stress that CMOs are civil servants whose job is to support the government. The scope and diversity of debates about the CMO role can be explained by its inherently contradictory nature, which requires incumbents to balance their commitments as physicians with their mandates as civil servants who advise and speak on the government's behalf. The long-haul COVID-19 pandemic has further tested the CMO role and has shone light on its varying remits and expectations across different jurisdictions, institutions and contexts. It is perhaps unsurprising, then, that calls to amend the CMO role have emerged in some jurisdictions during the pandemic. However, any discussions about changing the CMO role need a stronger understanding of how different institutional and individual approaches impact what incumbents feel able to do, say and achieve. Based on an ongoing comparative analysis of the position across five countries with Westminster-style political systems, we provide an overview of the CMO role, explain its prominence in a pandemic, examine some debates surrounding the role and discuss a few unanswered empirical questions before describing our ongoing study in greater detail.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.044 |
| Scholarly communication | 0.013 | 0.029 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".