Newfoundland and Labrador: 80/20 Staffing Model Pilot in a Long-Term Care Facility
Bibliographic record
Abstract
This project, based in Newfoundland and Labrador's Central Regional Health Authority, is the first application of an 80/20 staffing model to a long-term care facility in Canada. The model allows nurse participants to spend 20% of their paid time pursuing a professional development activity instead of providing direct patient care. Newfoundland and Labrador has the highest aging demographic in Canada owing, in part, to the out-migration of younger adults. Recruiting and retaining nurses to work in long-term care in the province is difficult; at the same time, the increasing acuity of long-term care residents and their complex care needs mean that nurses must assume greater leadership roles in these facilities. This project set out to increase capacity for registered nurse (RN) leadership, training and support and to enhance the profile of long-term care as a place to work. Six RNs and one licensed practical nurse (LPN) participated and engaged in a range of professional development activities. Several of the participants are now pursuing further nursing educational activities. Central Health plans to continue a 90/10 model for one RN and one LPN per semester, with the timeframe to be determined. The model will be evaluated and, if it is deemed successful, the feasibility of implementing it in other sites throughout the region will be explored.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".