Assessing the Quality of Deliberative Stakeholder Consultations with Pediatric Palliative Care Clinicians and Oncologists in Canada
Bibliographic record
Abstract
Abstract Objective—This paper presents findings from a quality assessment of deliberative stakeholder consultations with healthcare professionals in Canada on the implementation of a precision diagnostic for life-threatening pediatric brain tumors. Background—Advanced understanding of the basic biology and pharmacogenomics of pediatric brain tumors portend a clinical future in which oncologists base their clinical decisions in large part on results from laboratory derived tests (LDT) using next generation sequencing. Less is known, however, about how interprofessional healthcare teams perceive the opportunities and challenges of adopting LDTs in clinical practice, or how to best communicate LDT results to pediatric patients and their families. Deliberative stakeholder consultations present a promising alternative to traditional deliberative democratic methods. They allow researchers to unveil normative ethical and social values underpinning a forthcoming policy or new standard of care from the perspectives of key stakeholder groups, as well as make practical recommendations relevant for implementation.Methods—Using the DeVries framework for assessing the quality of deliberative process and information, we analyzed data from 44 post-consultation evaluation surveys from pediatric oncology and palliative care teams at two tertiary pediatric healthcare centers in Canada. Medians/means based on a 10-point Likert scale are reported. We also conducted turn-taking analysis and word-contribution analysis from the text transcriptions of each deliberation to assess equality of participation.Results—Deliberants agreed the quality of the deliberative process was fair (averages ranging from 9-10/10) and the opportunities to both receive expert information and discuss with others about the implementation of a new LDT were helpful (9.5/10). While the session improved understanding of the implementation barriers and opportunities, the session had marginal effects on deliberants’ perceived impact on their own clinical practice where median ratings ranged from 3-10/10. Participation was proportionate in at least four of the six deliberations, where no deliberant took more than 20% of total turns and contributed equal to, or less than 20% of total words.Conclusion—The quality assessments performed lend evidence to the informational value and overall fairness of the deliberative process achieved in this study to identify implementation and communication needs of healthcare professionals at the point when LDTs become standard for diagnosing life-threatening brain tumors in 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 | 0.040 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".