Elderspeak communication and pain severity as modifiable factors to rejection of care in hospital dementia care
Bibliographic record
Abstract
Abstract Background Rejection of care (RoC) occurs when persons living with dementia (PLWD) withstand or oppose the efforts of their caregiver. Improvements in hospital dementia care are needed, and one way to address this need is by identifying factors that lead to RoC, particularly those that are modifiable. Elderspeak communication is an established antecedent to RoC among PLWD in nursing homes. The purpose of this study was to extend these results to acute care settings by determining the impact of elderspeak communication by nursing staff on RoC by hospitalized PLWD. Methods Care encounters between nursing staff and PLWD were audio‐recorded, transcribed verbatim, and coded for semantic, pragmatic, and prosodic features of elderspeak. RoC behaviors was scored in real‐time using the Resistiveness to Care Scale. A Bayesian repeated‐measures hurdle model was used to evaluate the association between elderspeak and both the presence and severity of RoC. Results Eighty‐eight care encounters between 16 PLWD and 53 nursing staff were audio‐recorded for elderspeak and scored for RoC. Nearly all (96.6%) of the encounters included some form of elderspeak. Almost half of the care encounters (48.9%) included RoC behaviors. A 10% decrease in elderspeak was associated with a 77% decrease in odds of RoC (OR = 0.23, 95% CI = 0.03, 0.68) and a 16% decrease ( 0.84, CI = 0.73, 0.96) in the severity of RoC. A one‐unit decrease in pain severity was associated with 73% reduced odds of RoC (OR = 0.27, CI = 0.12, 0.45) and a 28% decrease ( 0.72, CI = 0.64, 0.80) in the severity of RoC. Conclusions Both elderspeak by nursing staff and RoC by PLWD are present and pervasive in acute care. Pain and elderspeak are two modifiable factors of RoC in hospitalized PLWD. Person‐centered interventions are needed that address communication practices and pain management for hospitalized PLWD.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".