Colourful Privacy: Designing Visible Privacy Settings with Teenage Hospital Patients
Bibliographic record
Abstract
Abstract—The paper reports from a qualitative study based on the analysis of semi-structured interviews and Participatory Design activities with hospitalised teenagers with chronic health challenges. We studied how teenage patients manage their online privacy, with a focus on the design and use of privacy settings. We found that the majority of participants preferred to visualise privacy settings through the use colours and to personalise access control. They also considered these necessary on more secure patient-centred social media. As proof of concept, we implemented some of the findings in a patient social network setting. We conclude that visualising and personalising privacy settings enable young patients to have more control over the sharing of personal information and may result in a more effective use of privacy settings. In addition, privacy-aware default settings may prevent teens from unintended sharing of personal information. Keywords-Facebook; participatory design; patient social media; privacy settings; teenage patients; visualisation of privacy I.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".