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 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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".