Designing Digital Mental Health Support for Paramedics Exposed to Trauma: Qualitative Study of Lived Experiences and Design Preferences
Bibliographic record
Abstract
Background: Paramedics face frequent exposure to trauma and intense occupational stress, often under conditions of limited psychological support and ongoing stigma. Digital mental health interventions have the potential to offer accessible, confidential, and tailored support. However, their acceptability and design must be informed by the lived experiences of paramedics to ensure effectiveness. Objective: This study aimed to explore the experiences of trauma exposure among UK paramedics in the workplace and their views on the design and delivery of digital mental health interventions. Methods: Semi-structured interviews were conducted with 22 UK paramedics. Participants were recruited through purposive and snowball sampling. Interviews were transcribed verbatim and analyzed using reflexive thematic analysis. Ethical approval was obtained, and trauma-informed principles were applied throughout data collection and analysis. Results: Five key themes were identified: (1) It Has to Feel Easy to Use: highlighting the need for digital tools that reduce cognitive burden and are accessible during unpredictable shifts; (2) Make It Fit My Needs: calling for interventions specifically designed for paramedics, with lived-experience-informed language and delivery; (3) We Need to Talk to Each Other: describing a strong desire for peer connection while recognizing barriers such as stigma and shift pressures; (4) I Need to Know It's Safe: emphasizes the importance of anonymity, data privacy, and psychological safety; and (5) Support Needs to Feel Human: reinforcing the value of integrating digital tools with human connection and professional services. Participants expressed strong support for an app-based solution that offers anonymity, rapid accessibility, and flexibility, while preserving opportunities for human interaction. Conclusions: Paramedics face unique mental health challenges that are not adequately addressed by existing services. Digital mental health tools offer promise if they are carefully co-designed to reflect the realities of frontline work. Anonymity, usability, peer connection, and integration with existing support systems are critical to engagement. These findings offer actionable insights for the development of trauma-informed, context-sensitive digital mental health interventions for emergency service workers.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".