The impact of Pathology Outreach Program (POP) on United States and Canadian high school students
Bibliographic record
Abstract
Given recent trends in National Resident Matching Program (NRMP) data, there exists a looming deficit of practicing pathologists. As such, the Pathology Outreach Program (POP) was established in 2018 in the United States, and in 2022 in Canada, to educate high school students about pathology and laboratory medicine to help curb this projected shortage. We present survey data gathered from several educational sessions hosted at high schools in the United States (U.S.) and Canada over a 5-year period comparing participants' perceptions and awareness of pathology both before and after each session. Using this data, we wish to highlight the positive impact of POP on increasing students' awareness and appreciation for careers in pathology or laboratory medicine. This data will also highlight the additional work that must be done to further boost public knowledge of laboratory medicine's contributions to patient care. We hope this project will lay the foundation for further improvements to laboratory visibility and inspire additional outreach efforts to mitigate a future workforce shortage.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".