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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".