Bibliographic record
Abstract
OBJECTIVE: To describe the composition and duties of local boards of health (LBOHs). DESIGN: An online and written survey was utilized for data collection. The survey included demographics, roles and responsibilities, orientation and training, and concerns and needs of LBOHs. SETTING: This article seeks to expand what limited information we have on the composition and duties of LBOHs as an important foundational step in analyzing the role of LBOHs in leveraging improved public health outcomes. PARTICIPANTS: In 2011, the mixed methods survey was sent to a random sample of 2420 LBOHs in the 41 states, which meet the definition of having LBOHs. MAIN OUTCOME MEASURE: The data represent responses from 353 LBOHs in 35 states. RESULTS: Elected officials appoint members of 68% of LBOHs. The average board consists of a 7-member, county-based LBOH made up primarily of males (60%) and whites (96%). Hispanics make up 9% of boards. The majority of LBOH chairs have a graduate degree but no formal education or experience in public health. Local boards of health report reviewing public health regulations as their most common power but list recommending the approval of the budget for the local health department as boards' most frequent activity in the past 3 years. CONCLUSIONS: LBOH members and chairs are more similar in demographics to the top executives at local health departments than the general population or the public health workforce. Most LBOH chairs, however, lack experience in public health, and a quarter or more of LBOHs do not use their powers to set or recommend health priorities as a mechanism to leverage better community health outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".