Equipping the next generation of clinicians for addressing conflict mental health: A role for Geopsychiatry
Bibliographic record
Abstract
The past two decades have seen a surge in violent conflicts worldwide, leading to 238,000 conflictrelated fatalities in 2022 alone [1].This challenges the perception that conflict is limited to 'unstable' regions, highlighting its global reach.Active wars in Ukraine, Sudan and Palestine have drawn attention onto conflicts even further.Beyond immediate death tolls, the enduring psychosocial impacts are profound, with conditions like Post Traumatic Stress Disorder (PTSD) affecting millions [2, 3].During its last World Health Assembly, the World Health Organization approved a crucial resolution to integrate mental health and psychosocial support (MHPSS) across all stages of emergencies, including conflicts, disasters, and humanitarian crises.The distant and recent experience from conflict zones raises doubt on whether local healthcare systems and external interventions have the needed skills and experience to deliver in times of crisis [4]. Current gaps in trainingPsychiatric training often lacks comprehensive education on conflict-related mental health.A US survey revealed only 20% of psychology doctoral programs offer trauma-focused training [5].Similarly, a Canadian study found significant gaps in preparing residents to handle warinduced trauma [6].Despite some programs like George Washington University's global mental health (GMH) initiative, which includes rotations in conflict zones, these are exceptions rather than the norm [7].The lack of training means that many clinicians are ill-prepared to deal with the complex mental health needs of populations affected by conflict.For example, traditional psychiatric training often focuses on PTSD without addressing other common conflict-related conditions such as depression, anxiety, and substance abuse.Furthermore, training programs rarely include components on cultural competence, which is crucial for effectively treating refugees and migrants who may have different expressions of psychological distress.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.016 | 0.032 |
| Insufficient payload (model declined to judge) | 0.053 | 0.015 |
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".