Characterizing Informatics Roles and Needs of Public Health Workers
Bibliographic record
Abstract
OBJECTIVE: To characterize public health workers who specialize in informatics and to assess informatics-related aspects of the work performed by the public health workforce. METHODS (DESIGN, SETTING, PARTICIPANTS): Using the nationally representative Public Health Workforce Interests and Needs Survey (PH WINS), we characterized and compared responses from informatics, information technology (IT), clinical and laboratory, and other public health science specialists working in state health agencies. MAIN OUTCOME MEASURES: Demographics, income, education, and agency size were analyzed using descriptive statistics. Weighted medians and interquartile ranges were calculated for responses pertaining to job satisfaction, workplace environment, training needs, and informatics-related competencies. RESULTS: Of 10,246 state health workers, we identified 137 (1.3%) informatics specialists and 419 (4.1%) IT specialists. Overall, informatics specialists are younger, but share many common traits with other public health science roles, including positive attitudes toward their contributions to the mission of public health as well as job satisfaction. Informatics specialists differ demographically from IT specialists, and the 2 groups also differ with respect to salary as well as their distribution across agencies of varying size. All groups identified unmet public health and informatics competency needs, particularly limited training necessary to fully utilize technology for their work. Moreover, all groups indicated a need for greater future emphasis on leveraging electronic health information for public health functions. CONCLUSIONS: Findings from the PH WINS establish a framework and baseline measurements that can be leveraged to routinely monitor and evaluate the ineludible expansion and maturation of the public health informatics workforce and can also support assessment of the growth and evolution of informatics training needs for the broader field. Ultimately, such routine evaluations have the potential to guide local and national informatics workforce development policy.
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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 | 0.043 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".