StigmaBeat: Collaborating With Rural Young People to Co-Design Films Aimed at Reducing Mental Health Stigma
Bibliographic record
Abstract
Little is known about the experience and impact of intersectional stigma experienced by rural young people (15-25 years) who have a parent with mental health challenges. The StigmaBeat project employed a co-design approach to create short films to identify and challenge mental health stigma from the perspective of young people who have experienced this phenomenon. The aim of this paper is to describe the co-design methodological approach used in StigmaBeat, as an example of a novel participatory project. We describe one way that co-design can be employed by researchers in collaboration with marginalised young people to produce films aimed at reducing mental health stigma in the community. Through describing the processes undertaken in this project, the opportunities, challenges, and tensions of combining community development methods with research methods will be explored. Co-design with young people is a dynamic and engaging method of collaborative research practice capable of harnessing lived experience expertise to intervene in social issues and redesign or redevelop health services and policies. The participatory approach involved trusting and implementing the suggestions of young people in designing and developing the films and involved creating the physical and social environment to enable this, including embedding creativity, a critical element to the project's methodological success. Intensive time and resource investment are needed to engage a population that is often marginalised in relation to stigma discourse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".