Application and content of minimum data sets for care homes: A mapping review
Bibliographic record
Abstract
Abstract Background Care home residents have complex needs, and minimum data sets (MDSs) provide a unique source of information on their health and wellbeing. Although MDSs were first developed to monitor quality and costs of care, they can make an important contribution to research. Aim To describe the research applications of data from care home MDSs, and identify key outcome variables and measures used. Design Mapping review of published empirical studies using data generated from minimum data sets in long term care facilities for older adults. Methods We performed a comprehensive search of electronic databases (Medline OVID, CINAHL, Embase and ASSIA), using bespoke search strategies to identify English language publications 2011 - 2024. Articles were screened by two independent reviewers. They were grouped by study topic and data (on publication date, country, MDS, outcome variables and specific items or measures) were charted without quality assessment. The key features of the data are described in a narrative synthesis. Findings Searches identified 18588 articles published 2011-2024, of which 661 met inclusion criteria. 72% were from the USA, 12% from Canada and the remaining 16% from four European countries, South Korea and New Zealand. The studies encompassed individual resident functioning (e.g. mobility, incontinence), health conditions and symptoms (e.g. depression, pain), healthcare in the home (e.g. prescribing, end of life care), hospital attendances and admissions, transitions to and from care homes, quality of care and systemwide issues. Measures used reflected the content of the major MDSs, but there was a mismatch between the importance of some topics to care homes (e.g. incontinence) and the range of published papers, and limited consensus over how to measure quality of life. Conclusions Care home MDSs are a unique resource to support study of care home residents and impact of interventions over time. They are a powerful resource when linked to other datasets, and as an adjunct to primary data collection This analysis may serve as an accessible guide to the content and applications of MDS, allowing researchers to consider the sort of questions that can be posed and the different components of resident care or experience that can be evaluated.
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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 | 0.075 | 0.306 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.041 | 0.039 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".