DEC-12 “The Hardest Decision I Ever Had”: Parent Decision Making About Tnf-Alpha Inhibitor Treatment
Bibliographic record
Abstract
Purpose: Parents’ treatment decisions in pediatric chronic disease are often complicated by tradeoffs between disease and treatment risks, as well as the difficulty of proxy decision making. The objective of this study was to describe the information and process parents use to make treatment decisions for their children with chronic conditions; using decisions about TNF-α inhibitor (TNFαi) treatment, which has risks of immunosuppression and malignancy, as a model. Method: We conducted semi-structured interviews with parents of children with Crohn’s Disease (CD) (n = 14) or Juvenile Idiopathic Arthritis (JIA) (n = 20) who had experience deciding about TNFαi treatment. Participants had made a decision within the prior year, been referred to the study BECause of difficulty in decision making or were in the process of making the decision. Interview questions, developed based on existing pediatric decision-making literature and the Ottawa Decision Support Framework, were focused on information used to make decisions, factors that influenced decision making and the decision timeline. We used thematic analysis for all coding and analysis. Coding structure was developed through multidisciplinary team review of the initial interviews. Two coders then coded the remaining interviews, compared coding, and resolved disagreements through discussion. Data were analyzed by thematic grouping and compared between CD and JIA. Result: For nearly all parents, the decision about TNFαi treatment was the most challenging medical decision they had made. However, parents of children with CD experienced more, and ongoing, stress and anxiety related to the decision. In both groups, parents sought information from multiple sources including health care providers, the internet and social contacts. They looked for information related to treatment effectiveness, side-effects and individuals’ experiences with such treatment. In CD, where the decision often occurred over weeks to months, information was most often used to help make the decision. In contrast, in JIA the decision was often made in a single clinic appointment and information was then used to confirm the parent’s choice. Conclusion: Even after a decision has been made, some parents are left with persistent information needs, long-lasting concerns and worry related to TNFαi treatment for their child. Providing parents with structured support, including treatment-specific information, during TNFαi decision making may lead to improved decision quality, decreased psychosocial distress and, ultimately, improved outcomes for their children
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".