Canadian Armed Forces Transition Group: Leading the Way for a Smooth Transition
Bibliographic record
Abstract
Transitioning out of the military can be a difficult time for many veterans and can be especially challenging for members who are ill and/or injured. The Canadian Armed Forces Transition Group (CAF TG) Satisfaction Survey was administered to ill and/or injured Canadian Armed Forces (CAF) members who had accessed the services of their local Transition Centre (TC) over a two-year period. At the request of senior leadership in the CAF TG, an infographic was subsequently created to provide CAF members with an overview of some of the key findings. While a full report on the survey methods and results is planned for the near future, the purpose of this paper is to make this infographic and a short narrative more accessible to a broader audience. 749 CAF members completed the survey yielding a response rate of 32%. Nearly three-quarters of respondents reported being satisfied overall with their local TC, and only 11% reported dissatisfaction. In line with this finding, respondents reported that their well-being had significantly increased since accessing TC programs and services. Of the nearly 50% of respondents who reported that they were transitioning out of the CAF, most were aware of the transition services available. Finally, the majority reported being satisfied with the transition services they had used, in that they rated these as relevant, complete, timely, and helpful in preparing them for their transition from the CAF to civilian life. Together, these results demonstrate the value and importance of the programs and services offered by the CAF TG and TCs in providing military members with a smooth transition out of the CAF.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".