Parents’ decision whether or not to enrol their infant in a clinical trial: towards a patient centred approach? A qualitative study
Bibliographic record
Abstract
Abstract Background Clinical trials are the cornerstone of drug evaluation but are difficult to perform in children since obtaining written informed consent from both parents is very challenging. We aimed to identify determinants of parents’ decision whether or not to enrol their child in a clinical trial. Methods A Grounded Theory qualitative approach was used, based on semi-structured interviews with parents who had to give their consent to enrol their child some years before in the TOSCANE study, evaluating the occurrence of chorioretinitis. An interview guide based on bibliographic references, expert consultations and work meetings with the TOSCANE investigators was used during video interviews, conducted until saturation was reached. Interviews were audio-recorded, transcribed anonymously into text format, and double coded before analysis. Results Between April 2020 and April 2021, 18 interviews (nine consenting and nine non-consenting parents) were conducted. Saturation was reached after 16 interviews. The important determinants of parents’ decision, already described in the literature and which could result either in consent or refusal, were: investigator perceived to be human and competent, parents’ personality, parents’ working in healthcare, strong preference for one of the treatment groups, good health of the child, opinions regarding research. New determinants, such as mothers’ guilt about toxoplasmosis transmission, were identified and mostly associated with non-consent. Conclusion Parents' decisions depend on a set of determinants related to family history, personality, and perception of the disease and research, none of them predominating. These determinants suggest that a patient-centred approach could be adopted along with the adequate training of investigators, which requires future assessment.
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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 | 0.047 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".