Considering consent: a structural equation modelling analysis of factors influencing decisional quality when accepting newborn screening
Bibliographic record
Abstract
INTRODUCTION: Newborn bloodspot screening (NBS) programs generate an ethical tension between promoting the uptake of effective public health measures and facilitating informed consent from individuals. AIM: To explore the factors that affect parental perceptions of decision quality when accepting NBS METHODS: Survey of parents with children screened in 2008 (n = 154, 32% response rate). Questions were based on previous research and existing measures. The primary outcome was decision quality. Predictors were latent constructs of Attitudes to medicine, Perceived knowledge, Attitudes to screening, and Perceived choice. Responses were analysed using structural equation modelling. RESULTS: Increases in perceived choice and positive attitudes towards screening improved decision quality. Perceived knowledge had a significant and positive relationship with attitudes to screening (0.375, p < 0.01) as did perceived choice on perceived knowledge (0.806, p < 0.01). Attitudes to screening were also significantly influenced by attitudes to medicine, although less so than the effect of perceived knowledge. The model had good fit on all indices (χ(2) = 61.396, df = 48, p = 0.093; CFI = 0.979; RMSEA = 0.043). CONCLUSIONS: Our results implicate the presentation of screening as a key determinant of decision quality both in terms of the immediate information regarding the potential benefits and risks, but also the way in which consent processes are managed. If we want to better understand parent decision-making we need to go beyond analyses of information content, or parental recall of this, but consider the context in which screening is provided.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".