From Silence to Surge: Nursing Home Research in Ireland Before and After the Pandemic
Bibliographic record
Abstract
Abstract Background Nursing home residents in Ireland experienced a disproportionate burden of illness and death during the COVID-19 pandemic, highlighting longstanding systemic deficiencies in governance, staffing, and research engagement. Prior to the pandemic, this vulnerable population, characterised by high levels of multimorbidity and disability, received limited research attention. A subgroup from the National Clinical Programme for Older People recommended the development of a research agenda in nursing homes, including resident involvement. This study aimed to characterise the extent and nature of Irish nursing home research from 1966 to 2024. Methods A bibliometric review was conducted using PubMed to identify publications related to Irish nursing homes from January 1966 to March 2020 (pre-pandemic) and April 2020 to July 2024 (post-pandemic). Data extracted included publication type, number of authors, institutional affiliations, countries of origin, disciplines involved, and acknowledgement of nursing home staff or residents. Descriptive analysis was performed using Excel and SPSS. Results A total of 144 publications were identified. Most papers (n=106; 73.6%) were published pre-pandemic, while 38 (26.4%) appeared in the shorter post-pandemic period, showing a substantial increase in publication rate (1.9 to 9.5/year). Original research comprised 81.3% of papers. Interdisciplinary authorship was common, yet only 12.5% of papers listed a nursing home as an author affiliation—primarily from public or voluntary sectors. Less than 40% of papers acknowledged staff or resident contributions. While COVID-19-focused publications increased markedly post-2020, broader topics in nursing home care remained underrepresented. Conclusion Despite increased research activity during the pandemic, engagement with nursing home research in Ireland remains limited, especially from the private sector. The lack of consistent stakeholder involvement and sustained research investment signals a need for a national strategy. Key priorities include implementing the interRAI tool, improving professional engagement, and enhancing research funding to ensure evidence-based policy and care for nursing home residents.
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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 | 0.097 | 0.224 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.022 | 0.030 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".