Bibliographic record
Abstract
by Barbara Murphy. Winnipeg, Canada, J. Gordon Shillingford Publishing, 2002, 140 pp. Why Women Bury Men is a short, informative, and easy-to-read book that examines the biological, behvioural, and lifestyle risk factors that contribute to the leading causes of mortality in Canadian men, resulting in a six-year longevity gap between men and women. Murphy states that the longevity gap is not considered a pressing public health issue since it is assumed that this gap is beyond anyone's control, a notion she questions. Through her analysis of different risk factors such as smoking, alcohol use, diet, exercise, and reckless driving, she demonstrates that the impact of biological factors on longevity is small in comparison to the behavioural, socioenvironmental, and lifestyle practices. Murphy is a social policy consultant and writer who has published other books on social issues that affect Canadians. Her writing style is clear and medical concepts are written in an easy-to-understand manner. Literature from a variety of disciplines is used to support her analysis, and an extensive bibliography is included. introductory chapter, Life Expectancies, Then and Now, traces the causes of mortality for women and men from the Stone Age until the present. Statistical data on the leading causes of death for males in Canada is included. Although the focus of the book is about the Canadian longevity gap, the author includes information and comparison to other countries that is extremely helpful. next five chapters explore the biological, behavioural, and lifestyle factors that place men at greater risk for earlier death. These factors are examined for their impact on the leading causes of death: heart disease, cancer, lung and liver diseases, and accidents. lifestyle practices of smoking, alcohol use, physical activity, diet, and stress are examined, and rates of these practices are compared to women and occasionally compared to rates in other countries. There is some repetition in these chapters, particularly with the risk factors of smoking, drinking, and diet. Chapter 6, The Risk-Takers, explores the concept of risk, how individuals perceive risk, why individuals take risks and how age and sex roles influence risktaking. This chapter could be helpful for practitioners working with males, but it fails to answer the key question of why men take more risks than women. Murphy indicates that male risk taking needs to be studied, since research findings could be useful in providing information on how to change risk-taking behaviours. …
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".