The Influence of Care Provider Access to Structural Empowerment on Individualized Care in Long-Term-Care Facilities
Bibliographic record
Abstract
OBJECTIVES: Implementing management initiatives that enable formal caregivers to provide quality, individualized care to older adults in long-term-care (LTC) facilities is increasingly important given that the number of LTC residents is projected to triple by 2031. The objective of this study was to explore the relationship between care provider access to structural empowerment and the provision of individualized care in LTC. METHODS: We computed structural equation models separately for registered nurses and licensed practical nurses (n = 242) and care aides (n = 326) to examine the relationship between access to empowerment structures (i.e., informal power, formal power, information, support, resources, opportunity) and the provision of individualized care. We subsequently undertook invariance analyses to determine if the association between empowerment structures and reported provision of individualized care differed between caregiver groups. RESULTS: Access to structural empowerment had a statistically significant, positive association with provision of individualized care for both groups. For registered nurses/licensed practical nurses and care aides, empowerment explained 50% and 45% of observed variance in individualized care, respectively. These notable percentages did not differ significantly between caregiver groups. DISCUSSION: Of the empowerment structures, support, especially in the form of access to educational opportunities and recognition for a job well done, seems to be particularly significant to care providers. Findings from this study suggest that provision of individualized care in LTC may be enhanced when formal caregivers have appreciable access to empowerment structures.
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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 | 0.004 | 0.029 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".