Bibliographic record
Abstract
The demographic shift toward older populations of physicians is well documented across much of the globe. As a result, it is becoming imperative that academic organizations generate research to inform understanding of both individual and institutional needs relating to these faculty members. The 2 reports by Skarupski and colleagues in this issue of Academic Medicine build on the research that is available, expose some new areas for consideration, and raise new lines of inquiry for researchers interested in studying late-career faculty and faculty transitions. The author of this Invited Commentary aims to situate Skarupski and colleagues' findings relative to what the academic medicine community knows-and does not know-about late-career faculty members, the institutions that employ these faculty, and the complex relationships therewith. Specifically, the author explores the following: the demographics of those considering retirement; the connection between identity and retirement decisions; the alignment between institutional and faculty member needs; institution preparedness; mentoring; and theoretical constructs and areas for inquiry that may inform future investigations.
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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".