Culture Change in Older Adult Care Settings: A Bibliometric Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: We systematically analyzed research on the culture change movement, in the context of global efforts to transform the provision of older adult care in institutional settings. RESEARCH DESIGN AND METHODS: Using Web of Science and Scopus publications relevant to person-centered care, culture change, or older adult care settings, we built bibliometric networks for keywords and terms extracted from titles and abstracts. Overlays depicted corresponding authors' countries, publication recency, funding, scientific impact, and concept use. RESULTS: The keyword network for 337 publications revealed variability in culture change settings and study indexing. Term network overlays showed geographical and chronological research variation. Corresponding authors from 14 countries contributed publications, mostly from the United States (69% of publications), Canada (9%), and Australia (5%). Social environment and person-centeredness studies, particularly in dementia care settings, were more recent than studies on physical environment, quality, organizational culture, turnover, and staffing. Scholars listed funding sources for 38% of publications; funding and scientific impact did not always overlap. Well-cited studies on standards of care and policy were funded at a lower rate than topics of lower impact. Over 60% of titles, abstracts, or keywords referred to quality and person-centeredness. DISCUSSION AND IMPLICATIONS: Originating in the 1990s in the United States, culture change quickly became an international phenomenon, drawing researchers' attention. Change research has deep roots in quality improvement and person-centered philosophy. We offered practical strategies for querying this hard to access literature. With some database-related limitations, empirical data on scientific impact can be used to allocate research funding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.235 | 0.261 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".