A Faculty Group Practice‐Driven Credentialing and Privileging Infrastructure in a School of Dental Medicine
Bibliographic record
Abstract
Credentialing and assigning clinical privileges are well-established practices in institutions that need to verify a clinician's ability to provide direct patient care services. The credentialing process verifies a provider's credentials to practice his or her profession, while privileging authorizes the individual to perform enumerated procedures within a specific scope of practice. All clinical faculty members at Harvard School of Dental Medicine (HSDM) practice in the Faculty Group Practice (FGP). Because of the number of practitioners in the FGP, the organization instituted a more formal process of credentialing that verifies that practitioners are not only licensed to practice, but also are competent to provide direct patient care. In contrast to other dental schools that have established similar protocols, HSDM approached the process not from the academic side, but rather from the clinical practice side, explicitly taking into account whether the FGP could accommodate another practitioner when an academic department wished to appoint a new faculty member. In doing so, we had to be careful to reconcile our educational and research needs with those of the FGP. In this article, we describe how, within this framework, we established a credentialing and privileging program in which all full- and part-time faculty members, as well as advanced graduate students, were included.
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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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".