Creating an Inclusive Definition for High Users of Inpatient Hospital Systems That Considers Different Levels of Rurality
Bibliographic record
Abstract
Multiple definitions have been used to identify individuals who are high system users (HSUs), through economic costs, frequency of use, or length of stay for inpatient care users. However, no definition has been validated to be representative of those residing in rural communities, who face unique service accessibility. This paper identifies an HSU definition for rural Canada that is inclusive of various levels of rurality, longitudinal patient experiences, and types of hospitalizations experienced. This study utilized the 2011 Canadian Census Health and Environment Cohort (CanCHEC) linkage profile to assess hospitalization experiences between 1 January 2009 and 31 December 2013. A range of common HSU indicators were compared using Cox proportional hazards modelling for multiple periods of assessment and types of admissions. The preferred definition for rural HSUs was individuals who are in the 90th percentile of unplanned hospitalization episodes for 2 of 3 consecutive years. This approach is innovative in that it includes longitudinal hospital experiences and multiple types of hospitalizations and assesses an individual's rurality as a point of context for analysis, rather than a characteristic. These differences provide an opportunity for community characteristic needs assessment and subsequent adjustments to policy development and resource allocation to meet each rural community's specific needs.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".