Informing a Canadian Skin Science Trainee Program Based on the State of Trainee Programs Offered by International Academic Societies
Bibliographic record
Abstract
BACKGROUND: For dermatology to effectively address the ever-growing medical needs, longstanding communication barriers across investigators working in different research pillars and practicing clinicians must be improved. To address this problem, trainee-specific programs are now evolving to align their educational landscape across basic science, translational and clinical research programs. OBJECTIVES: To establish a Skin Investigation Network of Canada (SkIN Canada) training roadmap for the career and skill development of future clinicians, clinican scientists and basic scientists in Canada. This Working Group aims to strengthen and harmonize collaborations and capacity across the skin research community. METHODS: The Working Group conducted a search of established international academic societies which offered trainee programs with mandates similar to SkIN Canada. Societies' program items and meetings were evaluated by use of an interview survey and/or the collection of publicly available data. Program logistics, objectives and feedback were assessed for commonalities and factors reported or determined to improve trainee experience. RESULTS: Through the various factors explored, the Working Group discovered the need for increasing program accessibility, creating opportunities for soft skill development, emphasizing the importance of current challenges, collecting and responding to feedback, and improving knowledge sharing to bridge pillars of skin research. CONCLUSIONS: Although improvements have been made to trainee education in recent years, a plurality of approaches exist and many of the underlying roadblocks remain unresolved. To establish fundamental clinician-basic scientist collaboration and training efforts, this Working Group highlights important factors to include and consider in building a trainee program and emphasizes the importance of trainee education.
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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 | 0.039 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".