Digitalizing a Brief Intervention to Reduce Intrusive Memories of Psychological Trauma: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has escalated the global need for remotely delivered and scalable interventions after psychological trauma. A brief intervention involving a computer game as an imagery-competing task has shown promising results for reducing the number of intrusive memories of trauma-one of the core clinical symptoms of posttraumatic stress disorder. To date, the intervention has only been delivered face-to-face. To be tested and implemented on a wider scale, digital adaptation for remote delivery is crucial. An important first step is to develop digitalized intervention materials in a systematic way based on feedback from clinicians, researchers, and students in preparation for pilot testing with target users. OBJECTIVE: The first aim of this study is to obtain and analyze qualitative feedback on digital intervention materials, namely two animated videos and two quizzes that explain the target clinical symptoms and provide intervention instructions. The second aim is to refine the digitalized materials based on this feedback. METHODS: We conducted semistructured interviews with 12 participants who had delivered or had knowledge of the intervention when delivered face-to-face. We obtained in-depth feedback on the perceived feasibility of using the digitalized materials and suggestions for improvements. Interviews were assessed using qualitative content analysis, and suggested improvements were evaluated for implementation using a systematic method of prioritization. RESULTS: A total of three overarching themes were identified from the data. First, participants were highly positive about the potential benefits of using these digital materials for remote delivery, reporting that the videos effectively conveyed key concepts of the symptom and its treatment. Second, some modifications to the materials were suggested for improving clarity. On the basis of this feedback, we made nine specific changes. Finally, participants raised some key challenges for remote delivery, mainly in overcoming the lack of real-time communication during the intervention. CONCLUSIONS: Clinicians, researchers, and clinical psychology students were overall confident in the use of digitalized materials to remotely deliver a brief intervention to reduce intrusive memories of trauma. Guided by participant feedback, we identified and implemented changes to refine the intervention materials. This study lays the groundwork for the next step: pilot testing remote delivery of the full intervention to trauma survivors.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".