Understanding Canadian stakeholders’ views on measuring and valuing health for children and adolescents: a qualitative study
Bibliographic record
Abstract
OBJECTIVE: Valuing child health is critical to assessing the value of healthcare interventions for children. However, there remain important methodological and normative issues. This qualitative study aimed to understand the views of Canadian stakeholders on these issues. METHODS: Stakeholders from health technology assessment (HTA) agencies, pharmaceutical industry representatives, healthcare providers, and academic researchers/scholars were invited to attend an online interview. Semi-structured interviews were designed to focus on: (1) comparing the 3-level and 5-level versions of the EQ-5D-Y; (2) source of preferences for valuation (adults vs. children); (3) perspective of valuation tasks; and (4) methods for valuation (discrete choice experiment [DCE] and its variants versus time trade-off [TTO]). Participants were probed to consider HTA guidelines, cognitive capacity, and potential ethical concerns. All interviews were recorded and transcribed verbatim. Framework analysis with the incidence density method was used to analyze the data. RESULTS: Fifteen interviews were conducted between May and September 2022. 66.7% (N = 10) of participants had experience with economic evaluations, and 86.7% (N = 13) were parents. Eleven participants preferred the EQ-5D-Y-5L. 12 participants suggested that adolescents should be directly involved in child health valuation from their own perspective. The participants were split on the ethical concerns. Eight participants did not think that there was ethical concern. 11 participants preferred DCE to TTO. Among the DCE variants, 6 participants preferred the DCE with duration to the DCE with death. CONCLUSIONS: Most Canadian stakeholders supported eliciting the preferences of adolescents directly from their own perspective for child health valuation. DCE was preferred if adolescents are directly involved.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
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 | 0.132 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".