Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Medical research is important for professional advancement, and mentoring is a key means by which students and early-career doctors can engage in research. Contrasting international research collaborations, research mentoring programmes are often geographically limited. As the COVID-19 pandemic has led to increased use of online technology for classes and conferences, a virtual, international approach to medical research mentoring may be valuable. APPROACH: We hereby describe our experience at the Cardiovascular Analytics Group, a virtual international medical research mentoring group established in 2015. We make use of virtual platforms in multi-level mentoring with peer mentoring and emphasise active participation, early leadership, an open culture, accessible research support and a distributed research workflow. EVALUATION: With 63 active members from 14 different countries, the Group has been successful in training medical students and early-career medical graduates in academic medicine. Our members have led over 100 peer-reviewed publications of original research and reviews since 2015, winning 13 research prizes during this time. IMPLICATIONS: Our accessible-distributed model of virtual international medical research collaboration and multi-level mentoring is viable and efficient and caters to the needs of contemporary healthcare. Others should consider building similar models to improve medical research mentoring globally.
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.
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 | 0.032 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.021 |
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 it