middle-aged men and suicide in Ireland
Bibliographic record
Abstract
Over the past 10 years, the suicide rate among middle-aged men (40-59 years old) in the Republic of Ireland has been the highest of all age cohorts. Self-Harm rates amongst middle-aged men have also increased in recent years, reaching a high of 207 per 100,000 in 2012. This is of particular concern, considering the higher lethality of suicide acts among males as well as the greater risk of suicide following self-harm amongst males. Despite these trends, there has been little attention on middle-aged men in public, policy or research discourse. Numerous studies have reported that economic recession and increased rates of unemployment are associated with a decline in mental health and increased rates of suicide and self-harm within a global, European and Irish context. These statistics indicate a clear and urgent need for a specific suicide prevention focus targeting middle-aged men. Suicide prevention is often understood in terms of risk and protective factors. This approach is necessary in order to determine and develop effective suicide prevention strategies and interventions. Gender encompasses socially constructed roles or normative behaviours for males and females. The key factors that are associated with gender and suicide among men are: • Men’s use of more lethal methods. • A reticence to seek help. • Higher rates of alcohol and substance misuse. • Factors specific to ‘high risk’ groups
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".