Classification of Emergency Departments According to Their Services for Community‐dwelling Seniors
Bibliographic record
Abstract
OBJECTIVES: The goal was to develop a classification of emergency departments (EDs) based on their organization of services for seniors discharged to the community. METHODS: This was a secondary analysis of data collected in a survey of key informants (chief physicians and head nurses) in EDs in Quebec on the organization of services for community-dwelling seniors discharged to the community. Organizational characteristics were classified a priori in the following three categories: 1) availability of human resources, 2) care processes, and 3) links to community services. A multifactorial analysis (MFA) was used to analyze the variables by category and globally, thus investigating not only the relationships between variables within each category, but also the relationships between different categories. The authors then proceeded to classify EDs using Ward's method (hierarchical ascendant classification) applied to reduced data dimensions. RESULTS: The sample consisted of 103 EDs. Analyses were carried out on data from the 68 (66%) of these EDs that supplied complete data. These 68 EDs did not differ in terms of their size or geographical location from the 35 other departments that supplied incomplete or no data. We identified three groups of EDs: most specialized (with regard to internal staff and care processes) and less community-oriented (n = 12), moderately specialized and less community-oriented (n = 28), and least specialized and more community-oriented (n = 28). CONCLUSIONS: This classification of EDs with respect to their organization of services for community-dwelling seniors may be helpful to those planning services, to decision-makers, and to researchers. The three groups of EDs identified in this study represent three types of organizations with differing assets and limitations. The generalizability of these groups to other settings and the implications for patient outcomes should be investigated.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".