A Gentle Introduction to Mental Health Through Storytelling: Design and Evaluation of Digital Human Library
Bibliographic record
Abstract
Stigma around mental health significantly hinders help-seeking behavior among university students, especially international students and those of Asian heritage. Digital storytelling can address this stigma by allowing individuals to share personal experiences, dispel negative attitudes, and foster community. However, current platforms often miss opportunities to engage users actively and supportively. To address this, we designed and implemented Digital Human Library (DHL), a story-sharing platform prototype, through brainstorming sessions with HCI researchers and a mental health nurse. We conducted a week-long diary study and semi-structured interviews with 16 university students to assess DHL's impact on self-efficacy toward seeking mental health help. Thematic analysis revealed that trust in professionals' and peers' stories can enhance self-efficacy but may be hindered if stories lack rich contextual details. DHL's support for gradual engagement helps users overcome initial reservations about seeking professional help and effectively helps them to think about future next steps, including becoming the storyteller themselves. This study highlights the potential for DHL to support mental health storytelling and suggests design implications for future HCI and CSCW research, emphasizing the importance of integrating supportive and engaging elements in digital platforms.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".