Non-Genetic Healthcare Providers’ Experiences and Perspectives with Rapid Genome-Wide Sequencing in Canadian Neonatal Intensive Care Units
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Rapid genome-wide sequencing (rGWS) continues to transform the care provided to infants with genetic conditions in neonatal intensive care units (NICUs). Previous research has demonstrated that rGWS has immense benefits on patient care; however, little is known about non-genetic healthcare providers' (HCPs) experiences and perspectives of working with rGWS and supporting families through the rGWS testing process in Canadian NICU facilities. To address this gap, we surveyed and conducted semi-structured interviews with non-genetic HCPs of diverse professions from NICUs in British Columbia. METHODS: An interpretive description approach was used to analyze interview transcripts to identify patterns and variations in non-genetic HCPs' experiences and perceptions with rGWS. RESULTS: Participants had varying degrees of exposure to rGWS and levels of comfort with the testing process. Numerous barriers affecting the implementation of rGWS were identified, including low levels of comprehension of rGWS, longer turn-around times than expected, and having to apply for provincial government approval to access testing. Participants desired more education on rGWS, clear guidelines on the use of rGWS in NICUs, and resources for non-genetic HCPs and parents to support implementation. CONCLUSIONS: The results from this study can inform the development of workflows and educational resources on the use of rGWS in NICUs, helping to ensure that the NICU team is supported to optimize rGWS implementation.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".