Using network analysis to map the formal clinical reporting process in pediatric palliative care: a pilot study
Bibliographic record
Abstract
BACKGROUND: Continuity of care is a key component of care in complex and chronic conditions. Despite its importance, it is often absent in chronic-disease management. One challenge has been identifying tools to measure care continuity. In one context important to families, namely pediatric palliative care, we undertook a project to identify continuity and to pilot the use of network analysis as a tool. METHODS: Network analysis studies patterns of relationships or interactions between members, providing qualitative and quantitative description of network structure. RESULTS: In this report we applied network analysis to paper records of clinical consultations and reports for 6 patients with complex conditions. A high degree of discontinuity was identified, and care was fragmented amongst specialist and generalist providers. Information was shared selectively and often moved in only one direction. CONCLUSIONS: Families have anecdotally reported frustration with poor continuity of care. Network analysis can be a useful tool in describing the discontinuity of care experienced by families dealing with complex and chronic conditions. This tool could be expanded to other systems such as electronic health records and many other health care situations.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".