The weight of office? A scoping review of mental health issues and risk factors in elected politicians across democratic societies
Bibliographic record
Abstract
BACKGROUND: The mental health and capacity to govern of democratically-elected politicians have become burgeoning topics of interest. Notably, in fulfilling demanding and high-stress roles, political officeholders could encounter distinctive risk factors, yet existing research literature about these subpopulations remains underexplored. AIMS: This scoping review aimed to systematically examine the breadth of available evidence on mental health issues and risk factors affecting democratically-elected politicians internationally 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 those studies reporting primary, empirical evidence on mental ill-health or risk factors with psychological correlates from serving politicians in "Full" or "Flawed" democracies (per Democracy Index) were included from 1999 to 2024. Titles and abstracts were screened and the full-texts of potentially eligible literature were assessed before extraction and synthesis. RESULTS: Eighteen sources met the eligibility criteria, cumulatively encompassing ~3,500 national, state, and municipal politicians across seven democracies (Australia, Canada, the Netherlands, Norway, New Zealand, the United Kingdom, and the United States). Cross-sectional surveys were predominantly utilized, with lesser use of mixed-methods approaches, qualitative interviews, and longitudinal cohorts. Violence emerged as a key concept, with twelve sources (66.7%) underlining its psychological toll and certain data indicating a disproportionate impact on female officeholders. Furthermore, four sources (22.2%) explored general psychopathology trends, revealing varying but sizeable mental ill-health and high-risk alcohol consumption rates, and two studies (11.1%) demonstrated adverse effects from specific occupational conditions. CONCLUSIONS: Current literature suggests that democratically-elected politicians can face complex mental health challenges. However, significant research gaps remain, including a paucity of prevalence estimates, longitudinal data, and intervention studies. Equally, the underrepresentation of most democratic countries accentuates the need for a more diverse evidence-base to better support the mental wellbeing of politicians worldwide.
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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 | 0.023 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.034 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".