Clinical public health: harnessing the best of both worlds in sickness and in health
Bibliographic record
Abstract
INTRODUCTION: Effective, sustained collaboration between clinical and public health professionals can lead to improved individual and population health. The concept of clinical public health promotes collaboration between clinical medicine and public health to address complex, real-world health challenges. In this commentary, we describe the concept of clinical public health, the types of complex problems that require collaboration between individual and population health, and the barriers towards and applications of clinical public health that have become evident during the COVID-19 pandemic. RATIONALE: The focus of clinical medicine on the health of individuals and the aims of public health to promote and protect the health of populations are complementary. Interdisciplinary collaborations at both levels of health interventions are needed to address complex health problems. However, there is a need to address the disciplinary, cultural and financial barriers to achieving greater and sustained collaboration. Recent successes, particularly during the COVID-19 pandemic, provide a model for such collaboration between clinicians and public health practitioners. CONCLUSION: A public health approach that fosters ongoing collaboration between clinical and public health professionals in the face of complex health threats will have greater impact than the sum of the parts.
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 | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.018 | 0.027 |
| 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".