Understanding how professionals cultures impact implementation of a pediatric oncology genomic test
Bibliographic record
Abstract
Purpose Healthcare innovation, exemplified by genomic medicine, requires increasingly sophisticated understanding of the interdisciplinary-organizational context in which new innovations are implemented. Deliberative stakeholder consultations are public engagement tools that are gaining increasing traction in health care, as a means of maximizing the diversity of roles and interests vested in a particular policy or practice issue. They engage participants from different knowledge systems (“cultures”) in mutually respectful debate to enable group consensus on implementation strategies. Current deliberation analytic methods tend to overlook the cultural contexts of the deliberative process. The paper aims to discuss this issue. Design/methodology/approach This conceptual paper proposes adding ethnographic participant observation to provide a more comprehensive account of the process that gives rise to deliberative outputs. To underpin this conceptual paper, the authors draw on the authors’ experience engaging healthcare professionals during implementation of genomics in the care for pediatric oncology patients with treatment-resistant glioblastoma at two tertiary care hospitals. Findings Ethnography enabled a deeper understanding of deliberative outcomes by combining rhetorical and non-rhetorical analysis to identify the implementation and coordination of care barriers across professional cultures. Originality/value This paper highlights the value of ethnographic methods in enabling a more comprehensive assessment of the quality of engagement across professional cultures in implementation studies.
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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 | 0.064 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".