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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".