Challenges of conducting research in long-term care facilities: a systematic review
Bibliographic record
Abstract
BACKGROUND: The aim of this review is to describe the challenges and barriers to conducting research in long-term care facilities. METHODS: A literature search was conducted in Ovid MEDLINE, Embase, Cochrane Central, PsycINFO and CINAHL. Keywords used included "long term care", "nursing home", "research", "trial", "challenge" and "barrier", etc. Resulting references were screened in order to identify relevant studies that reported on challenges derived from first-hand experience of empirical research studies. Challenges were summarized and synthesized. RESULTS: Of 1723 references, 39 articles were selected for inclusion. To facilitate understanding we proposed a classification framework of 8 main themes to categorize the research challenges presented in the 39 studies, relating to the characteristics of facility/owner/administrator, resident, staff caregiver, family caregiver, investigator, ethical or legal concerns, methodology, and budgetary considerations. CONCLUSIONS: Conducting research in long-term care facilities is full of challenges which can be categorized into 8 main themes. Investigators should be aware of all these challenges and specifically address them when planning their studies. Stakeholders should be involved from an early stage and flexibility should be built into both the methodology and research budget.
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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 | 0.191 | 0.448 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".