A Scoping Review on the Attributes of Cluster Randomized Controlled Trials in Long-Term Care Facilities
Bibliographic record
Abstract
Cluster randomized trial design, where groups of participants are randomized instead of individual participants, is increasingly being used in long-term care research. The purpose of this review was to determine the characteristics of cluster randomized trials in long-term care facilities. A medical librarian conducted the literature search. Two independent reviewers reviewed each paper. Studies were included if the design was cluster randomized and participants were from long-term care facilities. For each included study, two independent data extractors captured data on study attributes, including: journal, location, year published, author discipline, funding, methodology, number of participants, and intervention target. The literature search yielded 7,679 unique studies, with 195 studies meeting the selection criteria and being included for data extraction. The included studies were published between 1976 and 2017, with 53% of studies published after 2009. The term cluster randomized was in the title of only 45% of the studies. The studies were conducted worldwide; the United States had the largest number of studies (23%), followed by the United Kingdom (18%). Ten percent of studies were published in journals with an impact factor >10. The most frequent discipline of the first and last authors was medicine (34%), followed by nursing (17%). Forty-nine percent of the studies had government funding, while only 20% had medical industry funding. In studies with <1000 residents, 85% of the studies obtained consent from the resident and/or their proxy, while in studies with ≥ 1000 residents, it was 31%. The most frequent intervention targets were infection (13%), falls/fracture (13%), and behavior/physical restraint (13%). Cluster randomized controlled trials in long-term care have a unique set of characteristics. Results of this review will provide guidance to researchers conducting studies in long-term care facilities.
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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 | 0.383 | 0.765 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.019 |
| Bibliometrics | 0.032 | 0.042 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".