Volunteering behaviours among UK military Veterans during the COVID-19 pandemic and associations with health and well-being
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic facilitated new methods of and motivations for volunteering and created barriers to participation through social restrictions and lockdowns. The research assessing the volunteering behaviours of ex-service personnel (Veterans) is limited; however, as a group they may be more likely to volunteer because of aspects of military culture that encourage pro-social behaviours. The authors investigated levels of formal and informal volunteering among UK Veterans during the pandemic, factors associated with volunteering, and whether the pandemic affected Veterans' volunteering behaviours. Methods: An additional wave of data was collected from a longitudinal cohort study of the UK Armed Forces through an online survey conducted from June to September 2020. Participants were included if they had left the armed forces after regular service and were living in the United Kingdom. Invitation emails were sent to 3,547 Veterans, with a 44% response rate (N = 1,562). Results: Overall, 60% of Veterans reported volunteering in the past 12 months. Of those who volunteered, 41% reported formal volunteering, and 44% reported informal volunteering. Veterans reported reducing formal volunteering because of the pandemic (45%), but they also reported increasing informal volunteering (66%). Discussion: During the pandemic, UK Veterans volunteered at a level similar to the UK general population. They reported higher levels of formal volunteering and lower levels of informal volunteering compared with the UK general population. Understanding who among Veterans is likely to engage in volunteering could support future strategies to engage volunteers and open more opportunities for participation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".