Longitudinal program evaluation of an inter-institutional mentorship network for pediatric rheumatology using a quality improvement framework
Bibliographic record
Abstract
BACKGROUND: The American College of Rheumatology (ACR)/Childhood Arthritis and Rheumatology Research Alliance (CARRA) Mentoring Interest Group (AMIGO) is an inter-institutional mentorship program launched to target mentorship gaps within pediatric rheumatology. Initial program evaluation indicated increased mentorship access. Given the small size of the pediatric rheumatology workforce, maintaining a consistent supply of mentors was a potential threat to the longevity of the network. Our aims were to: (i) describe the sustainability of AMIGO over the period 2011-2018, (ii) highlight ongoing benefits to participants, and (iii) describe challenges in the maintenance of a mentorship network. METHODS: A mixed-methods approach centered on a quality improvement framework was used to report on process and outcomes measures associated with AMIGO annual cycles. RESULTS: US and Canada Pediatric rheumatology workforce surveys identified 504 possible participants during the time period. As of fall 2018, 331 unique individuals had participated in AMIGO as a mentee, mentor or both for a program response rate of 66% (331/504). Survey of mentees indicated high satisfaction with impact on general career development, research/scholarship and work-life balance. Mentors indicated increased sense of connection to the community and satisfaction with helping mentees despite limited perceived benefit to their academic portfolios. Based on AMIGO's success, a counterpart program for adult rheumatology, Creating Adult Rheumatology Mentorship in Academia (CARMA), was launched in 2018. CONCLUSIONS: Despite the challenges of a limited workforce, AMIGO continues to provide consistent access to mentorship opportunities for the pediatric rheumatology community. This experience can inform approaches to mentorship gaps in other academic subspecialties.
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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 | 0.072 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".