DEVELOPING AN INVENTORY ASSESSING NURSES’ BEHAVIOR TO MAINTAIN FUNCTION AMONG NURSING HOME RESIDENTS
Bibliographic record
Abstract
This paper outlines some of the most common trajectories experienced by older adults as they transition through long-term care (including home care, assisted living, and residential care). The overall sequencing of care transitions is considered along with the role of social, psychosocial and health factors in influencing them. Data are drawn from retrospective client assessments (RAI-MDS) recorded over a 4-year period (2008-2011) for clients aged 65 and over within the long-term care system in place in Vancouver, British Columbia, Canada. Latent Class and Latent Transitions Analyses reveal several different longterm care trajectories involving continuities as well as discontinuities in care. Each is associated with a somewhat different set of social, psychosocial and health factors. The findings attest to the complexity of the care transitions experienced in later life. Better understanding of these transitions should assist in predicting care needs, preventing unnecessary or untimely change, enhancing transitions that maintain quality of life, while also improving costeffectiveness and appropriate service utilization in different care settings.
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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 | 0.001 | 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".