Acceptance matters: Disengagement and attrition among LGBT personnel in the U.S. military
Bibliographic record
Abstract
LAY SUMMARY The U.S. military has undergone several changes in policies toward lesbian, gay, bisexual, and transgender (LGBT) service members over the past decade. Some LGBT service members report continued victimization and fear of disclosing their LGBT identity, which can affect retention of LGBT personnel serving in the military. However, there is little research on this population. This study uses data from a survey funded by the U.S. Department of Defense (2017-2018) and completed by 544 active-duty service members (296 non-LGBT and 248 LGBT) to better understand the career intentions of LGBT service members. Of transgender service members, 33% plan to leave the military upon completion of their commitment, compared with 20% of cisgender LGB and 13% of non-LGBT service members. LGBT service members were twice as likely as non-LGBT service members to be undecided as to their military career path. Lower perceived acceptance of LGBT service members in the workplace was associated with a higher risk of leaving among LGBT service members. Lower perceived unit cohesion was associated with attrition risk for all members, regardless of LGBT status. These findings suggest that the U.S. military can do more to improve its climate of LGBT acceptance to prevent attrition.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".