Building Professionalism Through Management Training: New England Public Health Training Center's Low-Cost, High-Impact Model
Bibliographic record
Abstract
CONTEXT: Evolving practices, accreditation, and priorities established in Public Health 3.0 are adding to the long-identified need for management training among public health practitioners. PROGRAM: The New England Public Health Training Center is addressing this need with a flexible, open-source, 16-topic training program. The program is designed to build competencies for current and future managers, preparing them for their day-to-day tasks and for the kinds of adaptation suggested by Public Health 3.0 advocates. IMPLEMENTATION: The training program uses live expert instructors for 10 webinars and 2 in-person trainings. Experts have also created the content for multiple self-paced E-Learnings that trainees undertake in addition to the instructor-led sessions. A webinar platform with breakout rooms and an advanced learning management system allows for online discussion and mentor interaction. The course has now been offered, evaluated, and modified 3 times, and the materials are available for noncommercial use by the public health community. EVALUATION: Using the Kirkpatrick training evaluation model, the recent cohort was satisfied (87.5%) with the training, reported identifying actions to apply information learned to their work (85.8%), and experienced statistically significant knowledge gains. Earlier trainees reported work-related behavior change. DISCUSSION: Management training offers the hope of increasing professionalism; creating better, more effective workplaces and programs; and preparing practitioners for an evolving public health landscape. Early results indicate that NEPHTC's program, Managing Effectively in Today's Public Health Environment, is a useful tool in realizing that hope.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".