Integrating paediatric subspecialists into the delivery of genomic medicine: A qualitative study
Bibliographic record
Abstract
Objectives: Genomic sequencing (GS) is increasingly recommended as a diagnostic test for patients with suspected genetic disorders, but access often remains limited to those referred to medical geneticists. Enabling paediatric subspecialists to access GS can expedite diagnosis for families and reduce burdens on the geneticist-led model of care. Targeted implementation strategies are needed to empower paediatric subspecialists to access GS; however, data to inform these strategies are lacking. Methods: Semi-structured interviews were conducted with 13 paediatric subspecialists (6 paediatric neurologists, 7 developmental paediatricians) and 9 genetics practitioners in Ontario, Canada, exploring barriers and facilitators to expanding access to GS amongst paediatric subspecialists. Interview guide development was informed by the Consolidated Framework for Implementation Research. Interviews were transcribed verbatim, coded inductively, and analyzed thematically. Results: Facilitators identified by interviewees included a tension for change, clinician motivation, and the presence of analogous infrastructure. The barriers to be addressed included logistical (requiring increased resource investment), cognitive (requiring upskilling and improved support for non-geneticist clinicians from genetics services), and cultural (requiring role clarification and trust-building between groups). Conclusions: To maximize readiness of paediatric subspecialists to access GS, implementation strategies must be designed to capitalize on facilitators and reduce barriers. Evaluation of such models will be essential to ensure they meet the needs of paediatric subspecialist end-users while delivering on the expected value of GS for patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".