Models of provider care in long-term care: A rapid scoping review
Bibliographic record
Abstract
INTRODUCTION: One of the current challenges in long-term care homes (LTCH) is to identify the optimal model of care, which may include specialty physicians, nursing staff, person support workers, among others. There is currently no consensus on the complement or scope of care delivered by these providers, nor is there a repository of studies that evaluate the various models of care. We conducted a rapid scoping review to identify and map what care provider models and interventions in LTCH have been evaluated to improve quality of life, quality of care, and health outcomes of residents. METHODS: We conducted this review over 10-weeks of English language, peer-reviewed studies published from 2010 onward. Search strategies for databases (e.g., MEDLINE) were run on July 9, 2020. Studies that evaluated models of provider care (e.g., direct patient care), or interventions delivered to facility, staff, and residents of LTCH were included. Study selection was performed independently, in duplicate. Mapping was performed by two reviewers, and data were extracted by one reviewer, with partial verification by a second reviewer. RESULTS: A total of 7,574 citations were screened based on the title/abstract, 836 were reviewed at full text, and 366 studies were included. Studies were classified according to two main categories: healthcare service delivery (n = 92) and implementation strategies (n = 274). The condition/ focus of the intervention was used to further classify the interventions into subcategories. The complex nature of the interventions may have led to a study being classified in more than one category/subcategory. CONCLUSION: Many healthcare service interventions have been evaluated in the literature in the last decade. Well represented interventions (e.g., dementia care, exercise/mobility, optimal/appropriate medication) may present opportunities for future systematic reviews. Areas with less research (e.g., hearing care, vision care, foot care) have the potential to have an impact on balance, falls, subsequent acute care hospitalization.
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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 | 0.081 | 0.199 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.043 | 0.050 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".