Exploring the ethical issues in research using digital data collection strategies with minors: A scoping review
Bibliographic record
Abstract
While emerging digital health technologies offer researchers new avenues to collect real-time data, little is known about current ethical dimensions, considerations, and challenges that are associated with conducting digital data collection in research with minors. As such, this paper reports the findings of a scoping review which explored existing literature to canvass current ethical issues that arise when using digital data collection in research with minors. Scholarly literature was searched using electronic academic databases for articles that provided explicit ethical analysis or presented empirical research that directly addressed ethical issues related to digital data collection used in research with minors. After screening 1,156 titles and abstracts, and reviewing 73 full-text articles, 20 articles were included in this review. Themes which emerged across the reviewed literature included: consent, data handling, minors' data rights, observing behaviors that may result in risk of harm to participants or others, private versus public conceptualizations of data generated through social media, and gatekeeping. Our findings indicate a degree of uncertainty which invariably exists with regards to the ethics of research that involves minors and digital technology. The reviewed literature suggests that this uncertainty can often lead to the preclusion of minors from otherwise important lines of research inquiry. While uncertainty warrants ethical consideration, increased ethical scrutiny and restricting the conduct of such research raises its own ethical challenges. We conclude by discussing and recommending the ethical merits of co-producing ethical practice between researchers and minors as a mechanism to proceed with such research while addressing concerns around uncertainty.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.188 | 0.389 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.029 | 0.022 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".