Mandatory Career Change: Transition Experiences of Canadian Armed Forces Veterans
Bibliographic record
Abstract
For all serving military personnel, transition to civilian life is inevitable; however, a release may come as a surprise for the approximately 2000 service members who retire annually as a result of a medical condition (Department of National Defence, personal communication, 28 June, 2017). These Veterans will leave the Canadian Armed Forces (CAF) under vastly different circumstances than those who voluntarily release. A medical release can be a result of a visible or non-visible wound suffered in an operational theater, a training injury or a chronic disease diagnosis. In a follow-up to landmark cross-sectional survey, Veterans Affairs Canada (2017a) concluded that 32% of releasing Veterans experience a difficult transition to civilian life; which, indicates the need for tailored transition services and research into the causes of these struggles. The trinity of an unexpected truncation of one’s career, a medical diagnosis coupled with an uncertain health status, and a precarious future signal the start of a soldier-to-civilian transition that many Veterans are ill-prepared to confront. Service members finding themselves on this trajectory are faced with a myriad of decisions that impact the totality of their lives. Aspects of military life are not limited to a predictable daily regime, rather service extends beyond the uniform affecting family and friends; a truly a unique way of life. One condition of military employment is that soldiers and families must move to various Canadian Forces Bases located in both major metropolitan areas with the associated high cost of living to rural locations with limited prospects for spousal employment, school choices for children, reduction of some community services offered in either French or English, or readily available access to family health care. When soldiers are leaving the CAF for civilian life, some of these considerations must be addressed to contribute to a successful transition. This study investigated the individual decision-making process surrounding a mandatory career change with a view to better inform policy-makers and transitioning Veterans. Findings indicate that a more deliberate institutional approach to transition will empower Veterans to realize their post-military potential with the assistance of a tailorable transition decision-making aid.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".