HemeOncoPoeisis: A fellow’s perspective on training.
Bibliographic record
Abstract
e23000 Background: The incidence of cancer is projected to increase 67% by 2030. Current projections suggest a 40% increase in demand for hematologists/oncologists (HO) yet only a 25% increase in trainees. This discrepancy between supply and demand represents an emerging challenge to public health. There are 3 types of training programs across the US: 1. Academic (69%), 2. Hybrid, which have both community and academic exposure (~15%), and 3. Community ( < 15%). The purpose of this study is to get feedback from fellows. Methods: Contact information was collected for 126 HO programs across the US and a short questionnaire was sent. Results: There were 36 respondents, 72% from academic programs, 20% from hybrid, and 8% were community based. 25%, 42%, and 33% were 1st, 2nd, and 3rd year fellows respectively. Only a quarter of the respondents had worked or pursued another fellowship prior to starting training and the majority were directly out of residency. Only 19% of the respondents indicated interest in practicing in the community setting, 30% wanted to practice in an academic-community hybrid, and 39% wanted to practice in an academic setting. Of note about 31% of academic fellows reported not attending any national meetings (ASH/ASCO/other) in the last 2 years, 71% of hybrid trainees reported attending more than two conferences, and 75% of the community trainees attended 1 or more national meetings. Most common concerns from trainees from all programs was workload, research support, and didactics. Most of the trainees (70%) felt prepared clinically, but only 40% felt prepared academically. See Table for additional results. Conclusions: Trainees in hybrid programs appear to be most satisfied with their training. Also, trainees in academic programs indicated they wanted more exposure to community settings for future jobs. A large HO workforce will be needed in the community setting yet there is a vacuum when it comes to community based HO training. Perhaps future directions in trainee education can be towards encouraging relationships between the academic and community centers to help trainees get a broad exposure in order to be prepared for the future demands the current projections predict. [Table: see text]
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.056 | 0.007 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".