The Public Health Workforce Interests and Needs Survey
Bibliographic record
Abstract
CONTEXT: Public health practitioners, policy makers, and researchers alike have called for more data on individual worker's perceptions about workplace environment, job satisfaction, and training needs for a quarter of a century. The Public Health Workforce Interests and Needs Survey (PH WINS) was created to answer that call. OBJECTIVE: Characterize key components of the public health workforce, including demographics, workplace environment, perceptions about national trends, and perceived training needs. DESIGN: A nationally representative survey of central office employees at state health agencies (SHAs) was conducted in 2014. Approximately 25,000 e-mail invitations to a Web-based survey were sent out to public health staff in 37 states, based on a stratified sampling approach. Balanced repeated replication weights were used to account for the complex sampling design. SETTING AND PARTICIPANTS: A total of 10,246 permanently employed SHA central office employees participated in PH WINS (46% response rate). MAIN OUTCOME MEASURES: Perceptions about training needs; workplace environment and job satisfaction; national initiatives and trends; and demographics. RESULTS: Although the majority of staff said they were somewhat or very satisfied with their job (79%; 95% confidence interval [CI], 78-80), as well as their organization (65%; 95% CI, 64-66), more than 42% (95% CI, 41-43) were considering leaving their organization in the next year or retiring before 2020; 4% of those were considering leaving for another job elsewhere in governmental public health. The majority of public health staff at SHA central offices are female (72%; 95% CI, 71-73), non-Hispanic white (70%; 95% CI, 69-71), and older than 40 years (73%; 95% CI, 72-74). The greatest training needs include influencing policy development, preparing a budget, and training related to the social determinants of health. CONCLUSIONS: PH WINS represents the first nationally representative survey of SHA employees. It holds significant potential to help answer previously unaddressed questions in public health workforce research and provides actionable findings for SHA leaders.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".