Leadership tasks in public health: findings from the National Board of Public Health Examiners’ job task analysis
Bibliographic record
Abstract
Introduction Public health leadership plays a crucial role in shaping effective health policies and practices. The National Board of Public Health Examiners (NBPHE) conducts a job task analysis (JTA) survey every 5–7 years to update the Certified in Public Health (CPH) examination. The objective of this study is to examine the JTA findings on leadership tasks in public health practice. Methods In April 2022, through the collaboration of expert panels and a validation survey, 103 tasks organized into ten domains were established for the JTA survey. The JTA survey was distributed online to current public health professionals. Across the tasks in the ten domains, respondents were asked about frequency (Scale of 1–6; how often they performed this task) and criticality (Scale of 1–5; how important this task was to their job). Results A total of 2,091 public health professionals responded to at least 82 of the 103 tasks (80%) and were included in the analysis. Approximately 86% of respondents worked in the United States and 41% had earned their CPH credential. Average frequency ratings ranged from 2.38 to 5.58, indicating that task ratings ranged from being performed never performed, every few years to daily. Average criticality ratings ranged from 2.46 to 4.64, indicating that task ratings ranged from not important to critically important. Specific to leadership, it was found that the ‘leadership’ domain ranked 2nd highest for both frequency and criticality. Conclusion Our findings suggest that leadership-focused development as part of academic public health programs and continuing education for the workforce is essential. Future research may examine how individuals perform on the leadership domain of the CPH exam across multiple characteristics to better inform additional workforce development strategies.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".