Mentoring New Research Center Directors Through Times of Uncertainty: The LEAD Program
Bibliographic record
Abstract
Abstract Mentorship is vital for new research center directors to help them build leadership skills, foster diversity, and lead successful teams, especially through times of uncertainty. However, there is limited discussion on mentorship programs to support new center directors in the field of gerontology. Collaborating with the Gerontological Society of America, we developed the Leadership Excellence and Advancement of Directors (LEAD) Program as a model to provide mentorship, professional growth, and peer support for center directors. This presentation aims to: 1) outline evolving mentorship strategies to empower new centre directors in navigating unpredictable challenges and uncertainty; and ii) identify the LEAD Program as an innovative model to provide mentorship to support new directors. The LEAD Program is a trailblazing GSA initiative that was developed in response to the growing call for mentorship from incoming directors as previous leaders transitioned to retirement. The LEAD Program is quickly evolving into a hub of mentorship by: i) addressing collective challenges and leadership needs; ii) providing peer support to engage in collective problem-solving; iii) facilitating knowledge to enhance decision-making during times of uncertainty; and iv) networking to support capacity-building among center directors. Moving forward, our next steps are to assess the LEAD Program to enhance the mentorship model for future cohorts of center directors.
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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".