Emergency room referrals to a geriatric outreach team: the analysis of referral reasons.
Bibliographic record
Abstract
The purpose of this study was to explore the referrals that were generated from one hospital emergency room to a community outreach team. This study used a specific geriatric program assessment team in Winnipeg, Manitoba, Canada as the community outreach team. Malcolm Payne’s description of Task Centered Casework and Crisis Intervention and Irene Pollin’s Medical Crisis Counseling served as the theoretical structure and design to gain an understanding of the reasons geriatric patients were referred to the GPAT on discharge. This study aimed to answer three research questions: 1) What is the emergency room medical team’s main reason for referral to a geriatric outreach team? 2) Are the referrals received from the emergency room medical team clearly identifying psychosocial issues as areas for examination by an outreach team? 3) Are psychosocial issues identified only after an outreach team clinician completed a comprehensive assessment? This study revealed that out of the 209 referrals to geriatric program assessment team, the highest number of times referred was for functional decline. It revealed that the emergency room medical team saw functional decline as a valid reason to have the geriatric program assessment team assess the individual in their home settings to ensure that any functional issue be addressed and possible adaptations made in a timely manner to avoid the reverberation that one ailment can set off. The second most common reason for referring to the geriatric program assessment team was for social issues. Forty percent of the referrals identified that there were concerns in relation to social issues which clearly identifies that the emergency room medical team identified psychosocial issues as an area for further examination. And finally, the findings show psychosocial issues were not identified only after an outreach team clinician completed a comprehensive assessment, but that both appear cognizant of the interplay between medical issues and social issues.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".