PATTERNS OF RUSHED AND UNFINISHED CARE AMONG CARE AIDES IN NURSING HOMES: A LATENT CLASS ANALYSIS
Bibliographic record
Abstract
Abstract Rushed care and unfinished care (also called missed care, care rationing) are common in nursing homes. Although the two phenomena are interrelated, most researchers have studied them separately and used a variable-centered approach. We took a person-focused approach and aimed to identify groups of care aides who rushed care and left care tasks unfinished in similar ways. This cross-sectional analysis used survey data from 3546 care aides working in a random sample of 87 urban nursing homes collected from September 2019 to February 2020 in three provinces in western Canada. We presented to care aides a list of physical and social care tasks (e.g., bathing, talking with residents). They answered whether or not they rushed or left a care task unfinished in their previous shift of work. We performed latent class analysis to identify patterns of rushed and unfinished care and verified the identified patterns with a different wave of data. A 4-class model emerged as the best-fit model for our data. Group 1 reported high levels of rushed and unfinished care, and Group 2 was low on both rushed and unfinished care. Group 3 reported high rushed care and Group 4 reported medium rushed care; both groups reported low unfinished physical care and medium unfinished social care. Our findings suggest the complexity of rushed and unfinished care among care aides. System-level support for this workforce is urgently needed to develop targeted interventions for sub-groups of care aides to reduce rushed and unfinished care in nursing homes.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".