Canadian military members’ experiences of using programs and services to support upcoming release: A qualitative study
Bibliographic record
Abstract
Introduction: The transition from military to civilian life begins with the decision to leave military service, marking the onset of a unique journey for each individual. The military-to-civilian transition (MCT) entails navigating through various programs and services, including those offered by the Canadian Armed Forces (CAF), Department of National Defence (DND), Veterans Affairs Canada (VAC), and civilian-based organizations. This article presents partial findings from a longitudinal study exploring the MCT experiences of Canadian military members, specifically focusing on the utilization of programs and services during peri-release. Methods: A qualitative, constructivist approach was used to delve into the diverse experiences of CAF members during MCT. Data collection occurred at three time points, with this study reflecting results from time point 1 (T1), conducted from May 2018 to January 2019. Semi-structured interviews and socio-demographic questionnaires were administered, with participation from both Regular Force CAF members and Reserve Force C service personnel. Results: At T1, 80 participants contributed insights, highlighting the benefits of existing programs such as those provided by VAC and DND/CAF transition groups. Additionally, suggestions were made to enhance these programs. Discussion: Participants accessed a variety of programs and services during the early stages of their MCT journey, largely reporting satisfactory support quality. Challenges identified include the volume and complexity of administrative tasks associated with accessing transition services and a lack of military cultural competency among civilian providers. Understanding these findings is crucial for addressing program and service-related challenges encountered by members during MCT.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".