PROMISES AND PERILS OF PERMANENT RESIDENT ASSIGNMENT IN RESIDENTIAL CARE FACILITIES
Bibliographic record
Abstract
Purpose: Permanent resident assignment (PRA) is the practice of assigning resident care aides (RCAs) to care for the same residents every shift they work. It has been touted as “the magic bullet” of culture change in residential care facilities (RCFs) and is considered by many to be essential to person-centred care. The purpose of this study was to explore how staff assignment practices affect the care giving experience from the perspectives of RCAs, residents, and family members. Methods: We conducted an institutional ethnography to explore the social organization of care in RCFs. The study was set in three RCFs: one with consistent PRA; one with PRA in one area of the facility and six week staffing rotations in another area of the facility; and one that had recently switched from PRA to three month staffing rotations. Data included 104 hours of naturalistic observation and 76 in-depth interviews. Results: The RCAs and residents described the primary benefit of PRA as being able to “get to know” each other well. Family members indicated that it assisted them in knowing who to go to when they had questions or concerns. However, RCAs also indicated that PRA had a negative impact on team work and diminished the exchange of individualized resident-care information amongst the care staff. Implications: Management initiatives are needed to ensure that the implementation of PRA does not result in the unintended consequence of diminishing staff members’ experience of teamwork or their ability and willingness to exchange pertinent, individualized resident-care information.
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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 | 0.065 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.020 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| 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".