Interprofessional meetings in geriatric assessment units: a matter of care organization
Bibliographic record
Abstract
Inpatient geriatric assessment units (GAUs) exist in Quebec, Canada, to deliver comprehensive, integrated care to older vulnerable patients. Most cases should be discussed at interprofessional meetings (IMs), but research has shown this not to be so for 39% of GAU patients. Consequently, a study was undertaken to (1) describe GAU team composition and (2) identify GAU and patient characteristics associated with case discussion at IMs at least once during a patient's stay. To this end, 877 hospitalization records from 44 GAUs were reviewed. Results showed most teams were composed of attending physicians, nurses, physical and occupational therapists, dietitians and social workers; 66% included clinical pharmacists and 43% liaison nurses. Multilevel modeling showed longer length of stay to be the strongest predictor of case discussion at an IM. Case discussion was also more likely for patients admitted via in- or inter-hospital transfer rather than via the emergency department, if the GAU included a liaison nurse, and if the GAU was not located in an urban area. In summary, case discussion at an IM depended more on how and where a patient was admitted than on the patient characteristics per se, suggesting that this is a matter of care organization.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".