Proposal Writing for <i>Healthy People 2010</i> : Veterinary Student Authors Win Hill’s Public Health Awards and DHHS Secretary’s Awards for Innovations in Health Promotion and Disease Prevention
Bibliographic record
Abstract
ince 1982, the United States (US) Department of Health and Human Services (DHHS), Health Resources Administration, in collaboration with the Federation of Associations of Schools of the Health Professions (FASHP), has sponsored the Secretary’s Award for Innovations in Health Promotion and Disease Prevention Writing Competition (Secretary’s Award). This competition illustrates DHHS’s dedication to health promotion and disease prevention initiatives by encouraging new ideas among health professions students throughout the United States. The Secretary’s Award encourages students to propose and/or implement innovative projects stressing health promotion or disease prevention, overarching goals established in Healthy People 2010.1 Before 1998, veterinary medical student participants in the Secretary’s Award competition received only lesser awards of “honorable mention.”2–4 During the 1998/1999 Secretary’s Award competition, however, two proposals from veterinary medical students won second place,5, 6 and one received the third-place award.7 Last year, during the 1999/2000 Secretary’s Award competition, veterinary medical students won two awards: the third-place award in the interdisciplinary competition and the secondplace award in the single discipline competition. Both of the veterinary medical winners of the 1999/2000 Secretary’s Award were previous winners of the Hill’s Public Health Award, a writing competition modeled on the Secretary’s Award but confined to veterinary medical students. This article will discuss the two writing competitions and briefly review the 1999/2000 award-winning proposals submitted by veterinary medical students.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.324 | 0.189 |
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".