Sustaining Clinical Academic Leadership and Excellence (SCALE): protocol for a mixed‑methods international consensus
Bibliographic record
Abstract
Introduction Clinical academics drive research, education, and innovation in health care, yet global reports highlight attrition, funding instability, and under-representation of primary care and community-based disciplines. Despite recognition of the problem, there is no internationally endorsed, prioritised strategy to strengthen this workforce. Objective The SCALE (Sustaining Clinical Academic Leadership and Excellence) study aims to develop a stakeholder-driven consensus statement that identifies and ranks actionable strategies to attract, retain, and advance clinical academics across specialties and career stages. Methods SCALE adopts a four-stage, mixed-methods approach: (1) a rapid scoping review of literature published since 2015; (2) an ethics-approved, REDCap-based pre-workshop survey gathering international stakeholders' ratings of draft statements and free-text feedback; (3) a Nominal Group Technique (NGT) session at the 2025 International Conference on Residency Education (ICRE) in Québec City with 18–22 purposively selected participants; and (4) a single-round electronic Delphi to validate and, where needed, refine the consensus outputs among the wider survey cohort. Consensus is pre-defined as a median rating ≥7 and an interquartile range ≤2 on a 1–9 importance scale. Quantitative data will be analysed descriptively; qualitative data will undergo reflexive thematic analysis. Reporting will align with ACCORD and CREDES guidelines. Impact This work will generate a globally relevant, context-sensitive roadmap to support clinical academic careers, with an emphasis on primary care and underrepresented disciplines.
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 | 0.163 | 0.150 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.150 | 0.038 |
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".