Pediatric Pathology Fellowship Recruitment—Report of a Survey Conducted by the Fellowship Committee of the Society for Pediatric Pathology
Bibliographic record
Abstract
Pediatric pathology (PP) is a subspecialty of pathology encompassing disease states during human development from the fetus to the young adult. Despite the existence of ACGME-accredited fellowship programs and opportunity for pediatric pathology subspecialty board certification, many pediatric pathology fellowship positions remain unfilled in North America. We sought to understand the difficulties in recruitment to the PP training programs by conducting a survey. A 3-pronged survey targeting pathology residents (PR), PP fellows and recent fellowship graduates (F&G), and PP training programs was conducted. Three separate questionnaires were prepared, one for each group; and administered online via SurveyMonkey. There were 175 responses to PR survey, 29 to F&G and 19 to programs survey. The results of the PR and F&G survey revealed that trainees select a subspecialty early in their residency training, primarily based on their interest, followed by prospects of employment. Nearly half of resident respondents had discounted pediatric pathology subspecialty training without prior exposure to the specialty. Senior residents and faculty members were reported as the main source for fellowship information for residents choosing subspecialty training and the choice of the training program was mostly dictated by geographic location. Most fellow recruits are racially diverse, female, and American medical graduates. Pathology residents decide on subspecialty training based on their interest; however, many are not exposed to pediatric pathology early on in training. The survey results suggest that existing PP fellowship positions likely will continue to exceed demand for subspecialty training. The results of the study could aid in developing strategies to boost recruitment to PP.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".