“Why Do I Need to Sign it? Issues in Carrying Out Child Assent in School-Based Prevention Research Within a First Nation Community
Bibliographic record
Abstract
The practice of and procedures for obtaining child assent in research involving children are based in Western conceptions of individual decision-making rights, free from any form of coercion including that of parents. In the context of obtaining assent for children involved in research in an Alberta First Nation, the issue can become more complex given respect for ethical frameworks based in collective decision-making and the responsibility of Elders and families to protect children in interactions with Western institutions. This article explores the results of a focus group held to discuss our experience with child assent in research taking place with a community-initiated and culturally-adapted substance abuse prevention program being taught in the community school. In this case the process of being asked to sign written individual assent in the classroom was perceived as bearing extrinsic risk. Given collective cultural norms, the communities past experiences with the safety of signatures, and the proper roles of Elders and family, the children asked “Why do I have to sign it” when asked to sign their assent for participation in the project. A process that involved gathering child assent with children surrounded by family and community was recommended. Greater researcher and REB responsiveness to the issue of non-malfeasance is needed, in this case, by not asking researchers and community members to act in ways that violate culturally-based ethical norms and protocol all of which are important to community continuity, self-determination, and well-being.
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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 | 0.104 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.035 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".