Supportive-leadership training to improve social connection: A cluster-randomized trial demonstrating efficacy in a high-risk occupational context
Bibliographic record
Abstract
The high, and still rising, rate of loneliness is a threat to public health (U.S. Surgeon General, 2023), with negative mental and physical health consequences (e.g., Holt-Lunstad, 2021). Given that loneliness is a risk factor for poor mental health, efforts to address loneliness are urgently needed. Workplaces can facilitate an employee’s social connection through supervisor support training, which can help mitigate loneliness. Among occupational groups, the military is at higher risk for mental health disorders, suicide, and loneliness (Fikretoglu et al., 2022; Naifeh et al., 2018). This study evaluated the efficacy of an evidence-based supportive-leadership training intervention targeting active-duty U.S. Army platoon leaders and targeting both proactive support behaviors that help bolster employee social connection, and responsive support behaviors, including destigmatizing mental health. Ninety-nine platoon leaders (69.7% of eligible leaders) completed the 90-minute training that consisted of both in-person and computer-based components. Using a cluster-randomized controlled trial design, intervention effects were tested using an intent-to-treat approach and revealed a significant effect, whereby loneliness of service members whose leaders were randomized to the intervention group (N=118) was significantly reduced compared to loneliness reports for service members in the control group (N=158). Additionally, service members with higher baseline loneliness were more strongly and positively impacted by the supervisor training, reporting higher levels of supportive behaviors from their leaders at 3 months post-baseline. In sum, these results suggest how workplaces, especially those that are considered high-risk occupations, and their leaders play a critical role in a national strategy to address Americans’ well-being.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".