Bibliographic record
Abstract
The author suggests the creation of expert-generalists to help provide the additional cost-effective access to care necessitated by increased insurance coverage under the Affordable Care Act. Expert-generalists, a concept drawn from an extant Canadian model, would be a cohort of primary care physicians who obtain additional training in a subspecialty area, which would widen their practice portfolio and bring enhanced infrastructure to primary care settings. Expanding the reach of primary care into the realm of more advanced subspecialty practice could be a way to enhance both access to and quality of care in a cost-effective fashion, in part because the educational framework for additional training already exists. Trainees could opt for an extra year of training after traditional residency or return to training after years in practice. Properly trained, an expert-generalist would benefit both the quality of the patient experience and the bottom line by expertly triaging patients to determine who will truly benefit from specialty consultations, decreasing specialists' engagement with cases that do not require their higher-tier care. The author considers the merits of this proposal, as well as potential objections and implementation challenges. It is suggested that this model be adopted incrementally, using demonstration projects that could assess the impact of an expert-generalist initiative on the physician workforce and on patients' access to quality primary and specialty care.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.053 | 0.012 |
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".