Quilted cellphilm method: A participatory visual health research method for working with marginalised and stigmatised communities
Bibliographic record
Abstract
The use of participatory visual methods and integration of cellphone technology is expanding in global public health research. Cellphilm method capitalises on these trends by inviting participants to use mobile devices to create short videos about health topics. This paper presents the quilted cellphilm method, which supports the participation of stigmatised populations to engage in research. We present the method with reference to the Celling Sex project, which worked with young women who have transactional sex experience. Four key steps in our unique model are discussed: (a) individual cellphilm-making; (b) participatory analysis; (c) creating a composite video; (d) publicly screening the work. We consider how working individually with participants in the cellphilm-making process built trust. We unpack how offering participants opportunities to engage in either group or one-on-one activities promoted participation in collaborative analysis. We outline how creating a composite video of the cellphilms and organising screenings facilitated knowledge translation and exchange. Overall, the quilted cellphilm method created a supportive community for vulnerable participants to generate products that challenged social stigma. Increased reliance on mobile media, especially during the COVID-19 pandemic, makes the quilted cellphilm method an opportune, exciting and accessible approach for participatory public health research.
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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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".