What is the association between obesity and the risk of suicide attempts and injuries in the Canadian and American female Population
Bibliographic record
Abstract
Background: Obesity is a significant problem for many developed and developing countries. Estimates indicate that 15% of the Canadian population and 30% of the United States population are obese. Previous studies have shown Body Mass Index (BMI) may be associated with either an increased or decreased risk of suicide attempts and injuries. Objective: We performed a structured literature review examining the association between obesity and the risk of suicide attempts and injuries in the Canadian and American female population. Methods: The following search "(suicid*) AND ((BMI) OR (obesity) OR (body mass) OR (overweight))", was made in PubMed providing 689 results. After applying limitations, inclusion and exclusion criteria, 8 relevant studies were identified and analysed. Results: Four article suggested no association between obesity and suicide attempts. Two articles suggest there may be a positive association between obesity and mental disorders that leads to the risk of suicide. The two remaining articles suggest that the risk of death from suicide is inversely related to BMI. Conclusion: Although the majority of findings conclude a positive association between obesity and the risk of suicide, there are few studies examining this relationship within the female population and contradictory conclusions of an inverse relationship are present in some of these studies. It is suggested that further research should be done.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".