Determinants of successful transition literature review
Bibliographic record
Abstract
Introduction: To understand the current Canadian research on transition from military to civilian life, the Office of the Veterans Ombudsman (OVO) initiated a literature review of peer-reviewed work from both academic and government sources, to help identify which factors contribute to a successful transition to civilian life for medically-released Canadian Armed Forces (CAF) Veterans. Methods: A scoping review was conducted by searching Embase, MEDLINE, PsycINFO, Google Scholar and the Veterans Affairs Canada (VAC) Research Directorate website for Canadian articles that described transition facilitators in any of the domains identified by the OVO. Results: In total, 94 relevant studies were identified and most of these focused on all Veterans without identifying a medical or non-medical release. Only 18 addressed the determinants of successful transition and only 2 focused on investigating transition issues for medically-released CAF Veterans compared to their non-medically-released peers. Discussion: The largest limitation found in the literature is the scarcity of articles related to identifying the factors that facilitate a successful transition for medically-released CAF Veterans. A further limitation is that the majority of surveys conducted were based on cohorts that had not experienced transition under the New Veterans Charter (NVC), implemented in 2006, or had only a small number of NVC subjects in their cohort. Given the significant policy and program changes implemented with the NVC, this is a compelling rationale for the requirement of more Canadian research to determine the effectiveness of the NVC policy and programs on transition outcomes for Canadian Veterans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".