P165: A Non-hierarchical mentorship model for professional development
Bibliographic record
Abstract
Introduction: Mentorship is an essential component of professional development and benefits include increased career satisfaction, scholarship, and efficiency of academic promotion. The Mastermind group, a collaborative, network-based model for mentorship has gained popularity in the business world. It comprises of a group of colleagues that provide mentorship and career advice for each other through regularly scheduled meetings. The group benefits from the combined intelligence and accumulated experience of the participants, who may be at different career stages. Methods: Academic Life in Emergency Medicine (ALiEM; www.aliem.com ), a digital health professions education organization, conducted two Mastermind groups for 14 team members in 2017. The groups included all levels of academic rank from full professor to instructors, and represented 14 different medical schools in North America. Each Mastermind group completed a self-assessment summarizing their professional strengths and weaknesses, two homework assignments, and two 90-minute videoconference meetings, using a structured, moderator-facilitated format. Meetings were conducted on Google Hangouts on Air© (Google Inc.). In the initial group meeting, participants discussed their self-assessments, current projects, and career challenges. The second meeting allowed discussion of suggested professional development resources for each participant, actionable next steps, and an accountability timeline for each participant. The free, cloud-based platforms and voluntary basis for the Mastermind groups resulted in a zero-cost innovation. Results: In a post-intervention survey, the 14 participants rated the experience as 9.4/10 (response rate 100%) using a Likert scale. In a quasi-experimental analysis participants cited the need for career advice or assistance with a project as their reason for participating. Participants received specific resource recommendations during the sessions, including books, training courses, or conferences. Contacts outside the group for additional mentorship were made possible given the breadth of networks among the participants. All participants had at least one identifiable next step with accountability to the group. Overall, the participants described a synergy of energy, commitment to one anothers longitudinal success, and benefit from the diverse range of talent and expertise in the group. Many of the members discussed plans to replicate this mentorship model at their own institutions. Conclusion: Our experiences suggest that the Mastermind conceptual framework is an easily replicated, feasible, zero-cost, and effective model for professional development. Though the model was originally proposed as a method for in-person discussions, we report a more modern, online experience for professional development in our diverse, globally-distributed team.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".