Time use during the military-civilian transition: Exploring concepts of occupational disruption, transition and balance
Bibliographic record
Abstract
Introduction Military-civilian transition (MCT) refers broadly to the process of leaving military employment and becoming re-established in the civilian world. The cultural differences between military and civilian spheres can create challenges for veterans as they make this transition.Objectives To explore the time use and experiences of Canadian Armed Forces members as they progressed through their release from the military.Methods Semi-structured interviews were conducted at two time points with the same participant group (T1 – within 6 months of release date and T2 – 6 to 12 months after the release date) as part of a larger qualitative MCT study. For this study, a secondary analysis of previously coded time use data was conducted using the software program MAXQDA to support our process. We framed our analysis using three theoretical lenses related to time use and MCT: occupational disruption, occupational transition, and occupational balance.Results Data from 75 English-speaking participants at T1 and 68 at T2 were included. Many participants demonstrated experiences of occupational disruption due to health limitations, occupational transition through changes in time use after leaving a highly structured career and seeking occupational balance, including meaningful time use with those who were important to them. There were diverse experiences across the participant group.Conclusions This study is the first to explore the time use experiences of Canadian military members and veterans during MCT. It identified challenges in filling time after leaving a highly structured career, with health limitations impacting time use options. Participants worked to balance demands of home and family, while valuing having discretionary time and spending it in meaningful occupations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".