Developing a formal model of peer support for bereaved military families: A co‐production and evidence‐based approach
Bibliographic record
Abstract
Abstract This study aimed to develop a formal model of peer support for bereaved military families in the United Kingdom (UK) that is co‐designed, inclusive, and integrated within the existing bereavement support system, without requiring affiliation with specific associations. The need for such a model was identified through a previous study (McGill et al., 2022), which examined the long‐term impact of military bereavement and recommended the development of peer support tailored to the short, medium, and long‐term needs of bereaved families. In response, the present research adopted an applied mixed‐methods approach, incorporating a systematic narrative review, online survey, expert consultation, and co‐production workshops. This iterative, multi‐phase process ensured the resulting pilot framework was grounded in the lived experiences of those affected by sudden or traumatic military loss. The findings informed the co‐production of an evidence‐based peer support model intended to enhance, compliment, and extend the nature and reach of existing bereavement services.
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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 | 0.096 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".