Long-Term Care Health Care Aides’ Perceptions of Decision-Making Processes in Transferring Residents to Acute Care Services
Bibliographic record
Abstract
Most transfers of long-term care (LTC) facility residents to the emergency department (ED) via 911 calls are necessary. Avoidable transfers can have adverse effects including increased confusion and dehydration. Around 20% of transfers are perceived to be avoidable or unnecessary, yet decision making around transfers is complex and poorly understood. Using a qualitative-focused ethnographic approach, we examined 20 health care aides' (HCAs) perceptions of decision processes leading to transfer using experiential interview data. Inductive analysis throughout iterative data collection and analysis illuminated how HCAs' familiarity with residents make them vital in initiating care processes. Hierarchical reporting structures influenced HCAs' perceptions of nurse responsiveness to their concerns about resident condition, which influenced communications related to transfers. Communication processes in LTC facilities and the value placed on HCA concerns are inconsistent. There is an urgent need to improve conceptualization of HCA roles and communication structures in LTCs.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".