Soldiering on only goes so far: How a qualitative study on Veteran loneliness in New Zealand influenced that support during COVID-19 lockdown
Bibliographic record
Abstract
Introduction: On April 25, 2020, Veterans’ Affairs in New Zealand (NZ) contacted approximately 3,000 of 8,000 known military Veterans by phone during the SARS-CoV-2 pandemic to ensure they were safe during the government-imposed lockdown. The impetus to this initiative were the findings of a cross-sectional quantitative survey of NZ Veterans, followed by the qualitative survey reported here, both carried out in 2019. The former report found 33% of 89 respondents were lonely and reported barriers to seeking support, and over half of Veterans felt uncomfortable accessing it. Methods: To understand the factors underlying loneliness, a qualitative survey was developed based on the barriers previously identified and a literature review. A purposeful sample based on gender, age, and ethnicity identified 20 respondents from the initial survey: 10 lonely and 10 non-lonely. Interviews were followed by an inductive thematic analysis, and themes and sub-themes were developed. Results: Ten of the 20 potential participants responded: 6 lonely and 4 non-lonely. Social and geographic isolation, problems with re-integration into the civilian community, and health problems were found to contribute to Veteran loneliness. Social connectedness, particularly to service peers, was the primary mitigating factor. Barriers included stoicism and perceptions of ineffective and inaccessible services. Inequity in the Veteran support system also emerged as a barrier for Veterans who had not deployed on operational missions. Discussion: During the pandemic, social connectedness will have decreased, and loneliness increased. Designing interventions with these factors in mind, and ensuring equity of access to support, should help combat Veteran loneliness.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".