Bibliographic record
Abstract
Objectives To assess recent disparities in fatal and non-fatal injury between travellers and the general population in Ireland. Design A cross-sectional population-based comparative study. Setting Republic of Ireland. Participants Population census and retrospective mortality data were collected from 7042 traveller families, travellers being those identified by themselves and others as members of the traveller community. Retrospective injury incidence was estimated from a survey of a random sample of travellers in private households, aged 15 years or over (702 men and 961 women). Comparable general population data were obtained from official statistical reports, while retrospective incidence was estimated from the Survey of Lifestyle, Attitude and Nutrition 2002, a random sample of 5992 adults in private households aged 18 years or over. Outcome measures Potential Years of Life Lost (PYLL), Standardised Mortality Ratios (SMR), Standardised Incidence Ratios (SIR) and Case Fatality Ratios (CFR). Results Injury accounted for 36% of PYLL among travellers, compared with 13% in the general population. travellers were more likely to die of unintentional injury than the general population (SMR=454 (95% CI 279 to 690) in men and 460 (95% CI 177 to 905) in women), with a similar pattern for intentional injury (SMR=637 (95% CI 367 to 993) in men and 464 (95% CI 107 to 1204 in women). They had a lower incidence of unintentional injury but those aged 65 years or over were about twice as likely to report an injury. Travellers had a higher incidence of intentional injuries (SIR=181 (95% CI 116 to 269) in men and 268 (95% CI 187 to 373) in women). Injury CFR were consistently higher among travellers. Conclusions Irish travellers continue to bear a disproportionate burden of injury, which calls for scaling up injury prevention efforts in this group. Prevention and further research should focus on suicide, alcohol misuse and elderly injury among Irish travellers.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".