Exploring Empathy and Compassion Using Digital Narratives (the Learning to Care Project): Protocol for a Multiphase Mixed Methods Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Digital stories—first-person, self-made, 2- to 3-minute videos—generate awareness, impart knowledge, and promote understanding on topics such as mental illness. Digital stories are a narrative-based art form often created by individuals without formal training in filmmaking to relate personal experiences. Somewhat like digital narratives, video testimonies created within the social marketing or fundraising campaigns of government agencies and private or public corporations aim to reduce the stigma of mental illness while supporting research and services. In video testimonies, personal stories are captured on camera by professional filmmakers. Sharing critical life events greatly benefits tellers and listeners alike, supporting catharsis, healing, connectiveness, and citizenship. OBJECTIVE This study explores digital stories and video testimonies featuring mental illness and recovery in their ability to elicit empathy and compassion while reducing stigma among viewers. METHODS Using mixed methods, phase 1 will involve a search of Canadian social marketing activities and fundraising campaigns concerning mental illness and recovery. Phase 2 will involve the organization of digital storytelling workshops in which participants will create digital stories about their own experiences of mental illness and recovery. In phase 3, a pilot randomized controlled trial will be undertaken to compare marketing and fundraising campaigns with digital stories for their impact on viewers, whereas phase 4 will focus on knowledge dissemination. RESULTS Ethics approval for this study was received in March 2021. Data on the feasibility of the study design and the results of the controlled trial will be generated. This study will produce new knowledge on effective ways of promoting mental health awareness and decreasing stigma, with practical importance for future social marketing and fundraising campaigns. The anticipated time for completion within the 2-year study period includes 9 months for phase 1 (knowledge synthesis activities identifying social marketing and fundraising campaigns) and phase 2 (storytelling workshops), 11 months for phase 3 (feasibility assessment and data collection: randomized controlled trial), and 2 months for phase 4 (knowledge dissemination). CONCLUSIONS The knowledge generated will have practical implications for the public and for future social marketing and fundraising campaigns promoted by government agencies as well as nonprofit and for-profit organizations by enhancing our understanding of how individuals and societies respond to stories of mental distress and what prompts citizens to help others. CLINICALTRIAL ClinicalTrials.gov NCT04881084; https://clinicaltrials.gov/ct2/show/NCT04881084 INTERNATIONAL REGISTERED REPORT PRR1-10.2196/33525
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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 | 0.052 | 0.046 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.082 | 0.019 |
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".