Feedback-guided Development for Patient Education Animation: HIV Transmission via Breastfeeding
Bibliographic record
Abstract
This thesis project uses animation to communicate the risk of HIV transmission via breastfeeding to mothers living with HIV in Canada. Current guidelines do not recommend breastfeeding for HIV+ mothers because there is always some level of risk. Knowledge of mother-to-child transmission is poor, and the cultural pressure to breastfeed has complex implications. It was essential that the science of transmission risk be conveyed in a clear and culturally sensitive manner, to allow women to make appropriate, informed decisions about whether or not to breastfeed. To accomplish this, we adopted a user-testing approach. Throughout development, the script, animatic, and character designs were presented for feedback to members of the target audience, healthcare providers, and representatives from Canadian HIV organizations in an iterative design process. At each round of feedback, the script, animatic, and visual assets were revised, and sent for further comment. Ongoing collaboration with the target audience helped us develop an animation with a wide diversity of characters, culturally sensitive metaphors, and nuanced descriptions of risk, in response to feedback that detailed desires about representation and identified how concepts were being misunderstood. User-testing approaches are necessary when creating patient education animations. Population needs, background, and context have a dramatic impact on patient understanding, and cannot be understood properly without user testing and direct feedback. Doing so helps prevent insensitive concepts and easily misinterpreted information, and thus is key to effective patient education animation.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".