How Can Primary Care Cross the Quality Chasm?
Bibliographic record
Abstract
The chasm between knowledge and practice decried by the Institute of Medicine (IOM) is the result of other chasms that have not been addressed. They include the chasm between what we know and what we need to know to improve care; the chasm between those who provide primary care and those who do not fund, study, support, or publish practical primary care studies; and the chasm between research and quality improvement (QI). These chasms are a result of problematic concepts, attitudes, traditions, time frames, and financing approaches among the various participants. If we are to facilitate the production and use of the knowledge needed for primary care to cross IOM's chasm, major changes are needed. These changes include the following: (1) admission by all primary care professions that we have quality problems that require our unified attention and action; (2) conversion of the paradigm from "translate research into practice" to "optimizing health and health care through research and QI"; (3) development and facilitation of more partnerships among clinicians, researchers, and care delivery leaders for engaged scholarship in both research and QI; (4) modification of the agendas and methods of funders and researchers so they emphasize the problems of patients and patient care and support practical time frames and research designs; and (5) facilitation by funders and journals of the dissemination and implementation of lessons from QI and practical research.
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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 | 0.087 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.020 | 0.035 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.022 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".