Socioeconomic Inequalities in Suicide and Suicidal Behaviour and Roles of Social Policy
Bibliographic record
Abstract
There is substantial evidence that suicidal behaviour is disproportionately observed among those with lower socioeconomic positions. Prior literature suggests that policy measures tackling severe socioeconomic deprivation may have impacts on decreasing the health gaps. Yet, little research has been conducted to examine the effects of social and welfare policies on suicidal behaviour and its inequality. This is in part because the understanding of the socioeconomic inequity in suicidal behaviour has been limited by the strong biological individualism, thereby overlooking the potential importance of social and welfare policies to tackle the population-level determinants of suicide. Using jurisdictional and temporal variations in social policies and patterns of suicide, this dissertation contributes to the literature by providing a summary of the current knowledge base of socioeconomic inequality in suicide and suicidal behaviour, identifying the knowledge gaps and future research questions, and adding novel evidence on the impacts of individual social policy and aggregate welfare generosity on suicide mortality and its inequality. In Chapter Two, consists of a scoping review of studies addressing socioeconomic inequalities in suicide and suicidal behaviour or the relationship between socioeconomic positions and different outcomes of suicidal behaviour in high-income countries with quality data. The literature is summarized to map the findings on the socioeconomic inequity in suicidal behaviour. The chapter concludes with an assessment of gaps in the current knowledge base and suggests a future research agenda. In Chapter Three, I examined whether relative welfare generosity in Canadian provinces is associated with overall suicide mortality and employment-based inequalities in suicide mortality by exploiting the provincial differences within Canada. In Chapter Four, I investigated the effects of two recent social pension reforms targeting older adults—Basic Old Age Pension (BOAP) and Basic Pension (BP)—implemented in South Korea on suicide mortality. Taken together, the findings of the dissertation contribute to the existing literature by mapping the relevant literature, identifying research gaps about socioeconomic inequalities in suicide, and suicidal behaviour, and examining the roles of social policy as a moderator of socioeconomic inequalities in suicide.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".