Exploring Mentorship as a Strategy to Build Capacity and Optimize the Embedded Scientist Workforce
Bibliographic record
Abstract
BACKGROUND: Mentorship plays a significant role in career development in academic and applied settings, but little is documented about its role in the experiential learning of academic trainees embedded in health system organizations. The experiences of the first cohort of Canada's Health System Impact (HSI) Fellowship program can provide insights into how mentorship in this innovative type of training can work. OBJECTIVES: To understand the mentorship strategies that were used and to explore fellows' and supervisors' perspectives and experiences on the effectiveness and value of those strategies. METHODS: Data from the surveys of fellows and their supervisors and a panel rooted in the lived experience of the first HSI Fellowship cohort were used. RESULTS: Health system and academic supervisors developed a range of innovative, individualized and effective approaches for guiding their fellows, such as providing the fellow with a committee of mentors within the organization, holding regular meetings with the fellow and both the health system and the academic supervisor and leveraging their own network to expand the network and resources available to the fellow. CONCLUSION: The results suggest that engaging senior leadership in health system settings has provided positive experiences for both fellows and their mentors.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".