NorthBEAT’s Capacity-to-Consent Protocol for Obtaining Informed Consent from Youth Evaluation Participants: An Alternative to Parental Consent
Bibliographic record
Abstract
Abstract: Ethical practice compels evaluators to obtain informed consent from evaluation participants. When those participants are minors, parental consent is routinely sought. However, seeking parental consent may not be appropriate in all evaluation contexts. This practice note presents one context (mental health services research in rural Canada) where seeking parental consent for youths’ participation in research was considered unethical and unfeasible. We present a two-step “capacity-to-consent” protocol that we developed to obtain consent from youth participants. This protocol offers an ethical and feasible alternative to seeking parental consent for youth. The implications for evaluation practice are discussed.
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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 | 0.318 | 0.394 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.059 | 0.018 |
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".