An evaluation plan for the Prince George's County rabies surveillance program: a utilization-based program evaluation design
Bibliographic record
Abstract
Within the Prince George’s County Health Department (PGHD) located in the state of Maryland is the Department of Communicable and Vector-Borne Disease Control (CVDC), which handles the County’s rabies surveillance activities. In Prince George’s County, all animal bites are required to be reported due to the risk of exposure to rabies, a viral disease that has a mortality rate of almost 100% once victims become symptomatic. According to CVDC records, in 2015 alone, there were a total of 1242 animal exposures. Not only is there a high volume of cases handled by the CVDC annually, but within the CVDC, only one staff member investigates potential rabies exposures full time. The proposed program evaluation utilizes a mixed methods approach. The quantitative method utilized will be a statistical analysis and the qualitative method will include interviews with the CVDC staff formally working with the rabies program, as well as members of the Prince George’s Police Department, Animal Management and a selected health care facility. The goal is that the implementation of this evaluation will allow the CVDC to gather information in order to evaluate the efficiency of its rabies surveillance program. Ultimately, the desired conclusion from this analysis is to be able to better understand how the program is operating and whether changes could be implemented to improve program function. Public Health Relevance: Due to the necessity and thus permanence of programs such as the Prince George’s County’s rabies surveillance program, as long as these programs are resulting in their desired end goal, how well these programs are being implemented is never evaluated. There is no evaluation of how the resources are being allocated, whether the program is well staffed or how well the program is able to work with it collaborators-essentially the efficiency of the program. Even though a program is achieving its designated goals, the implementation of the program is also important. A program that is functioning inefficiently can lead to a drain on already scarce monetary resources, burning out of program staff, and the unintended negative effects of policies put in place (or not put into place) without any concept of how they will affect the program on the ground. These are problems that can affect any type of program and eventually lead to problems in the long term-problems that can be avoidable through the use of program evaluation.
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".