Connected for health: Examining the use of a health‐related social media platform for children with chronic medical conditions
Bibliographic record
Abstract
BACKGROUND: Children with chronic medical conditions often experience limited opportunities for social experiences due to frequent hospitalizations and medical appointments. Computer technology can provide valuable opportunities for social inclusion through online communities. However, moderation has been shown to be an important component of safe and successful use. This paper explores the potential of a social media platform technology, Upopolis, to promote social connection and health-related management for children with chronic medical conditions, as well as the role of the coordinators who support children's use. METHODS: This study conducted qualitative in-depth, semi-structured telephone interviews with hospital-based child life specialists across Canada who acted as coordinators for Upopolis. Thematic analysis was employed on verbatim interview transcripts. RESULTS: Seven Upopolis coordinators (n = 7 female) from across Canada participated in six telephone interviews. Four themes were identified: First, Upopolis offered opportunities for connection (social and emotional) through receiving and giving support to others in similar situations. Second, Upopolis was considered safe and reliable for younger children (under 12) within the broader social media landscape. Third, Upopolis was a helpful resource for children to learn about medical diagnoses and procedures, as well as for expressing their experiences and reducing isolation. Fourth, participants identified that time, misuse of technology, and technical problems were challenges to the successful coordination of Upopolis. CONCLUSIONS: Children with chronic medical conditions were reported to use Upopolis to develop a greater social community. Specialized websites such as Upopolis have the potential to offer a safe online social networking opportunity where children can discuss what is happening to them, compared with other mainstream social media platforms. Given the increase in technology use in health and high usage of social media among children generally, these data can potentially inform the development and implementation of other specialist health-related online platforms for children with chronic medical conditions.
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 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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, 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".