Assessing the Feasibility of Using Parents’ Social Media Conversations to Inform Burn First Aid Interventions: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Burns are common childhood injuries, which can lead to serious physical and psychological outcomes. Appropriate first aid is essential in managing the pain and severity of these injuries; hence, parents who need timely access to such information often seek it from the web. In particular, social media allow them to reach other parents, hence these conversations may provide insight to aid the design and evaluation of burn first aid interventions for parents. OBJECTIVE: This study aims to determine the feasibility of finding, accessing, and analyzing parent burn first aid conversations on social media to inform intervention research. METHODS: The initial choice of the relevant social media was made based on the results of a parent focus group and survey. We considered Facebook (Meta Platforms, Inc), Mumsnet (Mumsnet Limited), Netmums (Aufeminin Group), Twitter (subsequently rebranded as "X"; X Corp), Reddit (Reddit, Inc), and YouTube (Google LLC). To locate the relevant data on these platforms, we collated a taxonomy of search terms and designed a search strategy. A combination of natural language processing and manual inspection was used to filter out irrelevant data. The remaining data were analyzed manually to determine the length of conversations, the number of participants, the purpose of the initial post (eg, asking for or offering advice), burn types, and distribution of relevant keywords. RESULTS: Facebook parenting groups were not accessed due to privacy, and public influencer pages yielded scant data. No relevant data were found on Reddit. Data were collected from Mumsnet, Netmums, YouTube, and Twitter. The amount of available data varied across these platforms and through time. Sunburn was identified as a topic across all 4 platforms. Conversations on the parenting forums Mumsnet and Netmums were started predominantly to seek advice (112/116, 96.6% and 25/25, 100%, respectively). Conversely, YouTube and Twitter were used mainly to provide advice (362/328, 94.8% and 126/197, 64%, respectively). Contact burns and sunburn were the most frequent burn types discussed on Mumsnet (30/94, 32% and 23/94, 25%, respectively) and Netmums (2/25, 8% and 14/26, 56%, respectively). CONCLUSIONS: This study provides a suite of bespoke search strategies, tailored to a range of social media platforms, for the extraction and analysis of burn first aid conversation data. Our methodology provides a template for other topics not readily accessible via a specific search term or hashtag. YouTube and Twitter show potential utility in measuring advice offered before and after interventions and extending the reach of messaging. Mumsnet and Netmums present the best opportunity for informing burn first aid intervention design via an in-depth qualitative investigation into parents' knowledge, attitudes, and behaviors.
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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 | 0.080 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".