Digital Media to Support Healing from Trauma: A Conceptual Framework Based on Mindfulness
Bibliographic record
Abstract
Digital media which involve narrative storytelling are increasingly used in nursing and health research, including clinical applications such as cinematherapy. A pilot study was conducted on how digital media self-accessed by marginalized individuals may be beneficial toward mindfulness and healing from trauma. Qualitative interviews were conducted with individuals (n = 8) who self-reported marginalizations via: race; gender; rural geography; socioeconomic status; indigenous or colonial experience; survivor of abuse; experiences of homelessness; or disability. Results indicated that trauma-narratives often organically emerge through discussions on digital media, with notable intersections with mindfulness-based practices and interventions. First, digital media can create a mindfulness of trauma as valid to discuss and disseminate. Mindfulness of authentic resolution also emerged as critical, as trauma may be employed in media narratives for attention or sympathy with no intent to support healing. Participant responses illustrate value in being mindful of individuals with severe trauma, who may be less likely to benefit from digital media. Digital media can foster sense-of-belonging and community-building amidst isolation; additionally, parasocial relationships may help foster supportive identities and ideologies on vulnerability. Findings are outlined in a preliminary conceptual framework, toward supporting future digital media with intent to create mindfulness or heal trauma.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".