Operationalization of assent for research participation in pre-adolescent children: a scoping review
Bibliographic record
Abstract
Abstract Background Seeking assent from children for participation in medical research is an ethical imperative of numerous institutions globally. However, none of these organizations provide specific guidance on the criteria or process to be used when obtaining assent. The primary objective of this scoping review was to determine the descriptions of assent discussed in the literature and the reported criteria used for seeking assent for research participation in pre-adolescent children. Methods Medline and Embase databases were searched until November 2020 using the term “assent” in the title or abstract. Inclusion criteria were (1) studies enrolling children which specifically described operationalization of the assent process and (2) studies of the assent process which provided a description of assent. Data collected included participant information, patient criteria for seeking assent, guidelines referenced, description of assent reported, how assent was obtained and assent information presented, and reported assent rate. For qualitative articles focusing on the assent process, important themes were identified. Results A total of 116 articles were included of which 79 (68.9%) operationalized assent and 57 studies (%) described the assent process. The most commonly reported criterion used to determine the ability of a child to assent was age (35.4%, 28/79). The reported minimal age for obtaining pediatric assent varied considerably across and within jurisdictions (5–13 years; median 7.5 years, IQR 7.0, 9.75). Cognitive ability was reported as a criterion for obtaining assent in 5.1% (4/79) of studies. Assent rates were only reported in 17.7% (14/79) of citations and ranged from 32.0 to 100%. Analysis of the 57 studies describing the assent process identified several themes, including age thresholds, assessment of capacity, variable knowledge of pediatric assent and parental roles. Conclusion We found significant variation in criteria used for assessment of patient capacity, delivery of information used to obtain assent and documentation of the assent process. While we acknowledge that individual children, settings and jurisdictions may require different approaches to obtaining assent, there should be agreement on important principles to be followed with resulting common guidance on assessing capacity, delivering information and documentation of the assent process for publication.
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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 | 0.093 | 0.309 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.025 | 0.024 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| 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".