Editors in Chief of <i>Public Health Reports</i> , 1878-2022: Men and Women Who Shaped the Discussion of Public Health Practice From 1918 Influenza to COVID-19
Bibliographic record
Abstract
Objectives: Public Health Reports ( PHR), the official journal of the Office of the US Surgeon General and US Public Health Service, is the oldest public health journal in the United States. Considering its heritage through the eyes of its past editors in chief (EICs), many of whom have been influential public health figures, can provide a fresh point of view on US public health history, of which the journal has been an integral part. Here, we reconstruct the timeline of past PHR EICs and identify women among them. Methods: We reconstructed the PHR EIC timeline by reviewing the journal’s previous mastheads and its articles describing leadership transitions. For each EIC, we identified dates in office, concurrent job titles, key contributions, and other important developments. Results: PHR had 25 EIC transitions in 109 years of its history, during which a single individual in charge of the journal could be identified. Only 5 identifiable EICs were women, who served as EIC for approximately one-quarter of the journal’s traceable history (28 of 109 years). PHR’s longest-serving EIC was a woman named Marian P. Tebben (1974-1994). Conclusions: PHR history revealed frequent EIC transitions and a low representation of women among its EICs. Mapping the timeline of past EICs of a historic public health journal can yield valuable insights into the workings of US public health, especially in the area of building a research evidence base.
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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".