The ‘6W’ multidimensional model of care trajectories for patients with chronic ambulatory care sensitive conditions and hospital readmissions
Bibliographic record
Abstract
OBJECTIVES: To synthesize concepts and approaches related to the analysis of patterns or processes of care and patient's outcomes into a comprehensive model of care trajectories, focusing on hospital readmissions for patients with chronic ambulatory care sensitive conditions (ACSCs). STUDY DESIGN: Narrative literature review. METHODS: Published studies between January 2000 and November 2017, using the concepts of 'continuity', 'pathway', 'episode', and 'trajectory', and focused on readmissions and chronic ACSCs, were collected in electronic databases. Qualitative content analysis was performed with emphasis on key constituents to build a comprehensive model. RESULTS: Specific common constituents are shared by the concepts reviewed: they focus on the patient, aim to measure and improve outcomes, follow specific periods of time and consider other factors related to care providers, care units, care settings, and treatments. Using these common denominators, the comprehensive '6W' multidimensional model of care trajectories was created. Considering patients' attributes and their chronic ACSCs illness course ('who' and 'why' dimensions), this model reflects their patterns of health care use across care providers ('which'), care units ('where'), and treatments ('what'), at specific periods of time ('when'). CONCLUSIONS: The '6W' model of care trajectories could provide valuable information on 'missed opportunities' to reduce readmission rates and improve quality of both ambulatory and inpatient care.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".