The care unit in nursing home research: Evidence in support of a definition
Bibliographic record
Abstract
BACKGROUND: Defining what constitutes a resident care unit in nursing home research is both a conceptual and practical challenge. The aim of this paper is to provide evidence in support of a definition of care unit in nursing homes by demonstrating: (1) its feasibility for use in data collection, (2) the acceptability of aggregating individual responses to the unit level, and (3) the benefit of including unit level data in explanatory models. METHODS: An observational study design was used. Research (project) managers, healthcare aides, care managers, nursing home administrators and directors of care from thirty-six nursing homes in the Canadian prairie provinces of Alberta, Saskatchewan and Manitoba provided data for the study. A definition of care unit was developed and applied in data collection and analyses. A debriefing session was held with research managers to investigate their experiences with using the care unit definition. In addition, survey responses from 1258 healthcare aides in 25 of the 36 nursing homes in the study, that had more than one care unit, were analyzed using a multi-level modeling approach. Trained field workers administered the Alberta Context Tool (ACT), a 58-item self-report survey reflecting 10 organizational context concepts, to healthcare aides using computer assisted personal interviews. To assess the appropriateness of obtaining unit level scores, we assessed aggregation statistics (ICC(1), ICC(2), η², and ω²), and to assess the value of using the definition of unit in explanatory models, we performed multi-level modeling. RESULTS: In 10 of the 36 nursing homes, the care unit definition developed was used to align the survey data (for analytic purposes) to specific care units as designated by our definition, from that reported by the facility administrator. The aggregation statistics supported aggregating the healthcare aide responses on the ACT to the realigned unit level. Findings from the multi-level modeling further supported unit level aggregation. A significantly higher percentage of variance was explained in the ACT concepts at the unit level compared to the individual and/or nursing home levels. CONCLUSIONS: The statistical results support the use of our definition of care unit in nursing home research in the Canadian prairie provinces. Beyond research convenience however, the results also support the resident unit as an important Clinical Microsystem to which future interventions designed to improve resident quality of care and staff (healthcare aide) worklife should be targeted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.114 | 0.172 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".