The weight of office? A systematic scoping review of mental health issues and risk factors in elected politicians across democratic societies
Bibliographic record
Abstract
Introduction The mental health status and capacity to govern of democratically-elected politicians have become significant topics of interest, which have attracted speculation in the media and beyond. In fulfilling demanding and high-stress positions, politicians could encounter distinctive risk factors that may harm their mental wellbeing, yet existing research literature about this topic remains underexplored. Objectives This scoping review aimed to systematically examine the breadth of available evidence on mental health issues and risk factors affecting democratically-elected politicians and to identify future research needs. Methods Using pre-defined eligibility criteria based on JBI guidelines, a systematic keyword search was conducted in May 2024 of MEDLINE, Scopus, and APA PsycNet, supplemented by snowballing techniques. Only studies reporting primary, empirical evidence on mental ill-health or risk factors with adverse psychological correlates from serving politicians in “Full” or “Flawed” democracies (per the Democracy Index) were included from 1999-2024. Titles and abstracts were screened and the full-text of potentially eligible literature was assessed before data extraction and synthesis. Results Eighteen sources met the eligibility criteria, cumulatively encompassing ~3,500 politicians across seven democracies, namely: Australia, Canada, the Netherlands, Norway, New Zealand, the United Kingdom, and the United States. Four sources (22.2%) explored general psychopathology trends, revealing varying but sizeable rates of mental ill-health and high-risk alcohol consumption. The other fourteen studies (77.8%) provided evidence on risk factors; twelve underlined the psychological toll of violence and two investigations highlighted the injurious effects of specific occupational conditions. Notably, exposure to violence often precipitated detrimental mental health outcomes, with certain data indicating a disproportionate impact on female officeholders. Conclusions Existing research literature suggests that democratically-elected politicians face considerable mental health challenges, especially from the effects of violence. However, there are notable research gaps with a paucity of reliable prevalence estimates, intervention studies, and work on national leaders. Equally, the underrepresentation of numerous democratic countries accentuates the need for a more diverse evidence-base to better support the mental wellbeing of politicians worldwide. Disclosure of Interest None Declared
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.
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 | 0.032 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.032 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".