Preliminary validation of the Barriers to Employment and Coping Efficacy Scales for Veterans with a mental health condition (BECES-V) – assessing barriers and self-efficacy to returning to work
Bibliographic record
Abstract
PURPOSE: Released Veterans with mental health conditions are three times more likely than civilians to experience limitations in work reintegration. Various tools have been developed to assess barriers impacting the return-to-work (RTW) process for Veterans transitioning to civilian life. The Barriers to Employment and Coping Efficacy Scales for Veterans (BECES-V) was designed to assess perceived barriers and self-efficacy among Veterans as they reintegrate the workplace following a prolonged absence. METHODS: This study offers a preliminary validation of the BECES-V tool, specifically investigating: the dimensions of RTW obstacles while considering the literature and employing concept mapping procedure, the salient RTW obstacles experienced by Veterans with mental health conditions transitioning from military to civilian workplaces in Canada and the USA, and the strongest dimensions of RTW obstacles and self-efficacy, using logistic regression analyses. The study involved 92 Veterans who completed the BECES-V. RESULTS: Health-related limitations and adaptability difficulties were salient in both countries; self-efficacy to overcome work-life balance difficulties, as well as mental health and military stigmatization, emerged as the strongest predictors of RTW. Utilizing BECES-V may help identify Veterans at increased risk for prolonged RTW, allowing rehabilitation professionals to address individualized obstacles and self-efficacy for successful RTW.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".