Application of protection motivation theory to clinical trial enrolment for pediatric chronic conditions
Bibliographic record
Abstract
BACKGROUND: Parents of children living with chronic but manageable conditions hope for improved therapies or cures, including Advanced Therapy Medicinal Products (ATMPs). Multiple pediatric clinical trials for ATMPs are underway, but the risk profile of ATMPs for chronic conditions is largely unknown and likely different than for terminal pediatric illnesses. Applying Protection Motivation Theory modified to the context of pediatric ATMP clinical trial enrollment, our study analyses information needs of parents of children living with chronic manageable conditions: Type 1 Diabetes (T1D) or Inherited Retinal Diseases (IRD). METHODS: We conducted semi-structured interviews with 15 parents of children living with T1D and 14 parents of children living with an IRD about: a) family background and the diagnostic experience; b) awareness of gene and stem cell therapy research and clinical trials for T1D and IRD; c) information sources on trials and responses to that information; d) attitudes to trial participation, including internationally; e) understanding of trial purpose and process; and f) any experiences with trial participation. We then discussed a pediatric ATMP clinical trial information sheet, which we developed with experts. We applied directed qualitative content analysis, based on PMT, to examine the information preferences of parents in deciding whether to enrol their children in stem cell or gene therapy clinical trials. RESULTS: Parents balanced trial risks against their child's ability to cope with the chronic condition. The better the child's ability to cope with vision impairment or insulin management, the less likely parents were to assume trial risks. Conversely, if the child struggled with his/her vision loss, parents were more likely to be interested in trial participation, but only if the risks were low and likelihood for potential benefit was high. CONCLUSIONS: Fear of adverse events as part of threat appraisal was the predominant consideration for parents in considering whether to enroll their child living with a manageable, chronic condition in a pediatric clinical trial of an ATMP. This consideration outweighed potential benefits and severity of their child's condition. Parents called for available safety data and fulsome communication processes that would enable them to make informed decisions about clinical trial enrolment on behalf of their children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".