Subspeciality training in hematology and oncology, 2003: results of a survey of training program directors conducted by the American Society of Hematology
Bibliographic record
Abstract
A survey of directors of adult and pediatric hematology/oncology subspecialty training programs in the United States and Canada was conducted to assess the environment in which recruitment and training is conducted in these medical disciplines. A total of 107 program directors responded to the survey, representing 66% of internal medicine and 47% of pediatric subspecialty programs in hematology or hematology/oncology. Specific areas covered in the web-based questionnaire included the type and demographics of the training program, profile of the training program director, characteristics of the applicant pool and existing trainee recruits, characteristics of the training program environment and curricula, research productivity of trainees, and the career pathways taken by recent training program graduates (including dominant areas of clinical interest). The results of this survey show considerable heterogeneity in the recruiting practices and the environment in which subspecialty training occurs, leading the authors to recommend improvements in or a heightened attention to issues, including recruitment of minority trainees, flexibility to recruit international medical school graduates, timing of trainee acceptance, maintaining the financial support of Medicare graduation medical education (GME), training of physician scientists, organization of the continuity clinic experience, visibility of nonmalignant hematology as a career path, and level of training program director support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".