Identifying contextual factors that impact community reintegration in injured female Veterans
Bibliographic record
Abstract
Introduction: Women are an ever-growing and integral part of the U.S. military. However, the research on community reintegration (CR) for injured female Veterans is limited. The purpose of this study was to identify the contextual factors influencing CR for injured female Veterans. Methods: Female Veterans reporting a physical or psychological injury acquired during military service ( N = 31) completed the Community Reintegration of Service Members’ Extent of Participation (EOP) and Satisfaction with Participation (SWP) subscales, the Craig Hospital Inventory of Environmental Factors, and the New General Self-Efficacy Scale to identify personal and environmental factors affecting CR. Statistical analyses were conducted to determine the clustering of participants on the basis of CR scores and the effect of environmental factors and self-efficacy on CR. Results: Levels of CR were organized into low, moderate, and high CR clusters. General self-efficacy was significantly related to CR, and a significant difference was observed between high and low CR clusters. CR was not related to time served in the military, total time deployed, history of suicidal ideation, or having a dependable social support system. Discussion: Findings indicate that general self-efficacy had the strongest relationship with CR for injured female Veterans. Results also suggest that participants had varying levels of CR, and those with lower levels of CR were more likely to perceive environmental factors as barriers to CR. Future research should explore the influence of environmental barriers on CR for injured female Veterans with a larger sample.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".