The Family Snapshot–Innovation to integrate family context into daily interactions in the <scp>NICU</scp>
Bibliographic record
Abstract
AIM: Current literature favours individualised decision making, an approach that requires understanding patients within their context and tailoring treatment and recommendations to their unique needs. In neonatology, family context becomes synonymous with patient context. In the neonatal intensive care unit (NICU), the team may be challenged to understand the intricacies of the family context, paramount for both families and clinicians. However, a significant gap exists between the intent to share information about the family context and the process of doing so. The transformational goal of this project was to embed an understanding of the family context into all interactions that occur in the NICU between clinicians and families, and between clinicians when discussing patients. METHODS: We designed and implemented the Family Snapshot (FS), an innovation to bridge the gap between the intent and the process to share the family context. RESULTS: Two groups of process measures have been collected to understand workflow integration: (1) whether the forms are being used and (2) how the forms are being used. Overall, completion of at least some part of the FS was >90%. CONCLUSION: This manuscript describes our process, its feasibility and impact and presents two tools, the FS antenatal consultations and the FS tab.
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".