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.
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".