Bibliographic record
Abstract
I completed my practicum as a Student Epidemiologist at the Durham Region Health Department with the Health Analytics and Research Team (HART). HART is tasked with conducting population health assessments in the Durham Region. As a member of HART, I played a critical role in assessing the maternal and infant health status of the local population using data from the Better Outcome Registry and Network (BORN) information system (BIS), a database that contains critical reproductive and child health data in Ontario. Specifically, my primary responsibilities included data extraction and data coding, which encompassed a series of tasks, spanning from data cleaning to data analysis in STATA. By analyzing BORN data, I was able to refine my statistical and programming skills. Moreover, I collaborated with public health program staff to develop infographics that disseminated critical maternal and infant health information to various communities. The infographics I assisted with covered a wide range of maternal and infant health topics, including issues such as gestational diabetes and maternal mental health concerns. From here, I was able to effectively communicate health information in a concise and understandable manner. In addition, I had the opportunity to attend various public health workshops and group meetings during my practicum. These experiences not only expanded my knowledge of diverse public health topics but also afforded me greater familiarity with public health practice. In summary, my time at the Durham Region provided me with an exceptional opportunity to apply the knowledge I gained in school to real-world practice. It was a valuable and enriching experience that significantly contributed to my professional development in the field of public health.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".