Academic Service Learning in Public Health
Bibliographic record
Abstract
The PUBH 489 course allows students to engage in community and/or campus based academic service learning related to public health. In Winter quarter 2021, CWU students were trained and certified in case investigation and contact tracing (CICT) for the COVID-19 pandemic. In conjunction with the Kittitas County Public Health Department (KCPHD), the CWU CICT team has been working to help limit the spread of COVID-19 in the community. The CICT team has been heavily involved in ensuring that COVID-19 positive students, and those identified as being close contacts, are following all recommendations for isolation or quarantine periods, respectively. As a student involved in the CICT team, I have been investigating positive cases to pinpoint infectious period timelines, determine isolation, and identify close contacts. Contacts are then notified and given instructions for quarantining, whether on-or-off-campus. Once cases and contacts have been investigated, each receives a daily call regarding general health and well-being, development of symptoms, and to determine if they are in need of any support or assistance from the University. This experience has allowed me to prioritize the well-being of my student community. I have been able to take the public health knowledge and skills I have learned throughout my education at CWU and apply that to something that is making a difference and giving me experience that I can utilize post-graduation.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.177 | 0.041 |
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".