In the Eye of the Beholder: A Stakeholder Analysis of the Value of the “Promotion in Place” Competency-Based Time-Variable Graduate Medical Education Pilot
Bibliographic record
Abstract
PURPOSE: Competency-based time-variable (CBTV) graduate medical education (GME) has been implemented in Canada, Europe, and the United States, yet its perceived value has not been explored. Promotion in Place (PIP) is a CBTV GME program in which residents graduating early advance to attending status with "sheltered independence" until the standard graduation date. This study describes perceived value of CBTV GME and PIP at Mass General Brigham by capturing diverse stakeholder perspectives. METHOD: In this stakeholder analysis using semistructured interviews (June 2022-August 2023), 49 participants were invited (44 representative members and 5 external stakeholders) from 11 GME programs: PIP eligible residents, program directors (PDs), chairs, service chiefs, and external national medical education organization leaders. Authors' understanding of value was informed by Harvey and Green's conceptualization of quality in higher education as "fit for purpose," "standards monitoring," "transformation," and "value for the money." Deductive codes and inductive subcodes captured diverse perspectives of value. RESULTS: Of the 49 invited stakeholders, 34 (69%) were interviewed across 5 stakeholder groups. Nearly all groups cited aspects of PIP that are "fit for purpose" as evidence of value; PIP supported "workforce readiness" and provided "sheltered independence" as intended. External stakeholders, PDs, service chiefs, hospital leadership, and faculty cited value aligning with "standards monitoring" (e.g., PIP must maintain or improve patient and resident outcomes). Nearly all groups cited aspects of PIP aligning with "transformation" as evidence of value. PIP promoted "independent decision-making" and enhanced trainee confidence. Chairs cited aspects of PIP aligning with "value for the money" (e.g., "cost neutral" as optimal for sustainability and avoidance of "hidden costs" such as assessment burden). CONCLUSIONS: Understanding perceptions of PIP and CBTV GME value is critical to engaging diverse stakeholders and extending CBTV GME to more programs and specialties. PIP's transformative nature underscores the added value of CBTV GME.
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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".