Combat Trauma after the Afghanistan War: An Analysis of the Need to Care for Canadian Soldiers through the Framework of Preference Ethics
Bibliographic record
Abstract
Part I Introduction The mental-health struggles of Canadian soldiers returning from the Afghanistan war have recently caught a lot of political attention.A Mental Health survey released in 2013 has determined that the rate of post-traumatic stress disorder among members of the Canadian Armed Forces (CAF) has nearly doubled since 2002 (Statistics Canada; The Globe and Mail).Julian Fantino, a retired police official, was recently forced out of his role as minister for Veteran Affairs Canada (VAC).This was as a result of numerous public complaints about his inability to effectively provide adequate healthcare to veterans.Mr. O'Toole, a retired Air Force officer in the Canadian military, was appointed Minister of Veterans Affairs in January 2015.Mr. O'OToole replaced Mr. Fantino who faced repeated opposition calls for his resignation or firing in the fall over his handling of the Veterans Affairs portfolio (CBC News, Jan 2015).The Veteran Affairs department has faced much criticism from some veterans because of the decision to close regional offices and for a lack of support for veterans with mental illness.Soldiers unable to function within the military due to mental illness do not have sufficient funds to care for themselves and their families after military service.According to Canadian military policy, soldiers who cannot achieve universality of service as per regulation DAOD 5023-0, must be dismissed.Natyncyck, the previous Chief of the Defence Staff, established a comprehensive approach to alleviate struggles of military families and soldiers suffering with combat stresses (Natyncyck 6).This endeavour to alleviate suffering families requires a Department of National Defence (DND), and a Government of Canada (GOC) commitment, through effective public policies and a well-integrated multi-disciplinary team of health professionals.However this comprehensive necessity has received much criticism ranging from long wait times for mental health services to limited numbers of mental health-care workers.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.030 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".