VET Connect: an emerging peer leadership program for Veterans on campus
Bibliographic record
Abstract
Currently, more than 1 million US Veterans are receiving Veterans Affairs (VA) education benefits to pursue college diplomas, advanced degrees, or vocational training. As increasing numbers of military members return home, colleges and universities must be prepared to support their transition to non-military educational and occupational settings. The VET (Veterans Embracing Transition) Connect Peer Leadership Program was designed to support student Veterans and assist them in transitioning to campus life. This study used a qualitative approach to examine the effects of VET Connect on Peer Leaders. Findings reveal that the program reduced participants' sense of isolation by connecting student Veterans to faculty and staff, to other student Veterans, and to the general student population. Participants reported that VET Connect promoted self-growth and integration, allowing them to transition to campus and civilian life. They reported developing skills such as public speaking and knowledge of campus resources, as well as insight into their emotions and self-acceptance. Participants also reported experiencing a renewed sense of purpose. Overall, findings suggest that VET Connect may serve as a potent high impact practice that engages Veterans in college and reduces the loneliness and distress that often accompany reintegration to the civilian world.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".