Different approaches to obtaining consent for follow-up result in biased samples
Bibliographic record
Abstract
BACKGROUND: Many injury studies use samples of hospital patients. If a study requires further contact, consent is usually required but the sample may be biased if many fail to consent. OBJECTIVE: To determine whether wording requiring 'active' consent resulted in more refusals than wording that was 'passive'--that is, where consent is implied in the absence of direct refusal. METHODS: Subjects were injured children seen in the emergency departments in five hospitals where the Canadian Hospital Injury Reporting and Prevention Program (CHIRPP) operates. For CHIRPP, parents or older children complete a one-page questionnaire to describe the injury; one question seeks consent to follow-up for research. Three of the hospitals use passive wording for this question and two use active wording. All cases where CHIRPP coordinators completed forms using the medical record were treated as refusals. It was hypothesised that there would be a significant association between the form of wording (active or passive) and the rate of consent, and that this association would be affected by socio-demographic and clinical variables. RESULTS: On average, 64.5% of parents gave consent for follow-up at hospitals using passive consent wording versus 42% where active consent wording was used. Passive wording consistently yielded a higher percentage of consents for all variables. The differences were greater than 5% for families living in census tracts with low median household incomes and greater than 10% for those age 15-19 years. For parent completed forms the adjusted OR for active wording was 0.48 (95% CI 0.43 to 0.54). CONCLUSION: Caution is needed when interpreting results from studies using hospital samples, especially when wording of consent for follow-up requires direct affirmation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.448 | 0.551 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".