107 Video interviewing children and young people for research purposes
Bibliographic record
Abstract
Background Inclusivity of children in research is vital, especially if findings are likely to impact services designed to promote their health and wellbeing. There are ethical concerns about using technology as a tool for data collection with children, which may explain a limited evidence base. The aim of this abstract is to highlight experiences using this technology to encourage further research in this area. Method ALPS (the heArt sibLings imPact Study) explored experiences of children who had a brother/sister with congenital heart disease. ALPS took place during the COVID-19 pandemic, necessitating use of a virtual platform. Interviews were undertaken with 17 children aged 8-17 years on Zoom. Data were collected from children across the UK through charities between September 2020 and February 2021. Parents were gatekeepers to most contact with children prior to interview. A Quick Response (QR) code was added to adverts linking to a short YouTube video designed for children to easily access study information. Prior to gaining assent children were asked about their understanding of ALPS. Paper forms were sent prior to interview, signatures were witnessed online, and forms returned in self-addressed envelopes. Results Video interviews with children in their home environment helped to build rapport, as the researcher-initiated conversation about participants’ surroundings including toys, books and photographs. Providing comfort during times of distress was more challenging but additional ethical provisions were planned to reduce any distress. This included parent presence after interview and contact after interview to check if support services were required for the child or family. Conclusion Using video interview platforms can be a useful way to limit participant and family burden and may be cost effective approach for research teams. Evidence based, secure and safe ways to gain meaningful assent and consent online should be explored further with this population.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".