Development and Validation of A Scheduled Shifts Staffing (ASSiST) Measure of Unit-Level Staffing in Nursing Homes
Bibliographic record
Abstract
Purpose of the study: To (a) describe A Scheduled Shifts Staffing measure (ASSiST) to derive care aide worked hours per resident day (HCA WHRD) at facility and unit levels in nursing homes, (b) report reliability through comparisons to administrative staffing data; (c) report validity by examining associations between HCA WHRD, staff outcomes (job satisfaction, emotional exhaustion), and resident quality indicators (QIs) (e.g. falls, delirium, stage 2+ pressure ulcers), and (d) explore intrafacility variation in staffing intensity levels related to unit-level variation in resident and staff outcomes. Design and Methods: We used data from 40 care units in 12 Canadian nursing homes between 2007 and 2012. Descriptive statistics and tests of association and difference described relationships of two measures of staffing with resident and staff outcomes. Results: Annualized rates of HCA WHRD from both data sources compared well at the facility level (Pearson Product Correlation; R = 0.847, p < .001), and were correlated similarly to staff work life and many QIs. Using ASSiST data, we show that staffing levels can vary by up to 40% at the unit-level within nursing homes. Implications: ASSiST is easy to collect, more timely to retrieve than administrative data, has good criterion and construct validity, and reflects intrafacility variation in health care aide staffing levels.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".