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Record W2800198149 · doi:10.3138/jmvfh.4313

Determinants of successful transition literature review

2018· article· en· W2800198149 on OpenAlexaffvenueabout
Steve Rose, Elizabeth G. VanDenKerkhof, Michael P Schaub

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2018
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsQueen's University
FundersU.S. Department of Veterans Affairs
KeywordsPsycINFOVeterans AffairsCharterGovernment (linguistics)MedicineMEDLINETransition (genetics)Political scienceFamily medicineGerontologyPsychologyLaw

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.344
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2018
Admission routes3
Has abstractyes

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