Evaluation of the Use of an Intervention by Health Care Providers for Resident Transfers from Long-Term Care to Emergency Departments in Times of Medical Urgency: A Qualitative Study
Bibliographic record
Abstract
Approximately 25% of older adult residents who experience an acute change in health status are transferred from Long-Term Care (LTC) to Emergency Departments (ED). We explored the use of an intervention (i.e., LTC to ED) care and referral pathway, INTERACT® Change in Condition cards, and STOP AND WATCH tool, in informing decision making regarding resident transfers. We conducted 22 semi-structured interviews with Health care Providers (HCPs) involved in the LTC to ED care pathway in Western Canada. Thematic analysis of the qualitative interviews was used to evaluate the use of the pathway and tools. We identified six themes influencing decision making around resident transfers including interprofessional practice and conflict, ambiguous and clear medical cases, ageism, health care providers' goals, family involvement in resident care, and intervention tools. The intervention may be useful in streamlining, documenting, and increasing transparency in complicated LTC resident care and transfer decisions.
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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 | 0.048 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".