A resident-centered approach to nursing staff planning in long-term care using the Synergy tool
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Synergy tool was used to forecast long-term care (LTC) resident needs for nurse staffing planning. Forecasting techniques were applied to longitudinal resident needs data from 67 unique residents across four units in two LTC homes in British Columbia. Acuity and dependency needs scores were forecasted for a four-week period using an Error Trend Seasonality (ETS) model. The four units demonstrated changing or stable resident acuity and dependency needs during the forecasting period. Increasing, decreasing, or stable resident acuity needs during the forecasted period would suggest the unit would benefit from higher, lower, and similar regulated nurse staffing level than the previous period respectively. Increasing, decreasing, or stable resident dependency needs during the forecasted period would suggest the unit would benefit from higher, lower, and similar PSW staffing level than the previous period respectively. This integrated technique sheds light on proactively planning staffing needs based on considerations of resident needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it