Youth leadership in mental health: Views from EFPT and IFMSA
Bibliographic record
Abstract
The world today is more challenging than ever before. Discrimination, stigma, and ever-changing lifestyles are just a few examples of elements that have a profound impact on the mental health status of our global population. Even though the burden of mental illness is well documented and increasing, mental health remains a neglected area of health worldwide. Youth Associations, like the International Federation of Medical Students’ Associations (IFMSA) and the European Federation of Psychiatric Trainees (EFPT) recognize the importance of tackling this problem, taking an active role on promoting education in our communities, tackling stigma and advocating for more action. Medical students worldwide, from Slovenia, Australia, Lebanon, Brazil, Quebec and Grenada – among at least 42 other countries, organise expansive, creative and engaging mental health projects. With particular interest we can mention the winner of the last Rex Crossley Award, attributed to a Slovenian project ‘in Reflection’: a suicide prevention project, which tackles the different factors associated with vulnerable groups through a series of workshops and campaigns that seek to destigmatize the mental health problems and offer the opportunity to high school students to get the help they need. This talk will give an insight into strengths, weaknesses and challenges faced by youth in tackling mental health, specially in the role of the IFMSA, displaying some of our most interesting and innovative projects from future mental health leaders around the world, together with the initiatives of EFPT. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.011 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".