Which primary care specialty? Factors that relate to a choice of family medicine, internal medicine, combined internal medicine-pediatrics, or pediatrics.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This study was conducted to examine factors used by medical students to select a primary care specialty that may differentiate students who choose the primary care specialties of family medicine, internal medicine, pediatrics, and combined internal medicine-pediatrics. METHODS: A questionnaire was sent to all family physicians and an equal number of other primary care physicians graduating from one of 24 medical schools in 1997-1999. Twelve schools had increasing proportions of graduates choosing family medicine in this study period, and 12 had decreasing proportions. The questionnaire asked about factors related to choice of specialty, which could be grouped into the specialty domains of type of patients, process, content, and setting. RESULTS: For family physicians, the most important factor was patient relationships, and the second most important was wanting an approach to the practice of medicine similar to that of family physicians. Internists indicated as most important wanting to work with adults and as the next most important an "internal medicine approach" to the practice of medicine. Most important for pediatricians was working with children and next most important was having patient relationships like other pediatricians. Those in combined internal medicine-pediatrics most often indicated a desire to work with children and next most important was an approach to medicine like others in their specialty. CONCLUSIONS: The most important reasons for choice of specialty were similar for all primary care specialties and related to congruence between the graduate and the physicians in the specialty or the process of providing care within that specialty. The factors that differentiated the four specialties related to the content of the specialty.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".