END-OF-LIFE PAIN FOR NURSING HOME RESIDENTS: THE ROLE OF HEALTHCARE AIDES AND CONTEXTUAL FACTORS
Bibliographic record
Abstract
Pain management is a hallmark of quality end-of-life care. This presentation will define pain trajectories in nursing home (NH) residents’ last six months of life, and show how these trajectories are influenced by health care aides (HCAs) and their working environment. This observational study utilizes the RAI-Minimum Data Set (MDS) linked to the TREC Measurement System (TMS) survey. MDS provides resident-level longitudinal data on pain plus various clinical measures. TMS captures point-in-time metrics on HCA supply, their characteristics (e.g., time rushed, feelings of empowerment) and their working environment (e.g., team leadership, care culture). Data are available on a representative sample of NHs from Western Canada. Data were analyzed on 982 residents in their last six months of life. Pain levels were negligible for 60.6% of residents during this time, and increased substantially or remained high for 34.4%. The effect of HCAs and contextual factors on these pain trajectories is discussed.
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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 | 0.002 | 0.002 |
| 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".