ArticleInternational journal of prisoner health2022
Large-scale evaluation of a computer-based learning program to increase prison staff knowledge on geriatric and end-of-life care.
Article in International journal of prisoner health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Capacity-building strategies that support correctional and justice health professionals to provide best-evidenced based healthcare for people in prison: a systematic review.International journal for equity in health · 2025Pooled it
- Supporting Older, Frail, and Vulnerable Adult Prisoners Through Peer Care: Observations, Reflections, and Recommendations Based on a UK/U.S. Researcher Exchange Visit.Journal of correctional health care : the official journal of the National Commission on Correctional Health Care · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
purposeThe purpose of this study is to continue research and development of the ECAD-P learning system with an emphasis on developing a scalable unit for testing in a larger number of more diverse correctional settings. There are almost 2.3 million US persons incarcerated. Geriatric and end-of-life (EOL) care in corrections is not as equitable as care in the free world. Technological delivery of geriatric training to staff through computer-based learning (CBL) offers a novel approach to improve care and reduce disparities among those who are most vulnerable during confinement. DESIGN/METHODOLOGY/APPROACH: This mixed methods study built an interactive CBL for multidisciplinary staff to address EOL and geriatric issues in prisons. The CBL was iteratively built and tested prior to launching a full-scale evaluation using a pre/post-intervention design.
findingsEvaluation of the CBL occurred at 7 sites (i.e. 6 state prisons and 1 prison health-care vendor). A total of 241 staff were recruited with 173 completing post-tests. Outcomes were knowledge acquisition regarding care for aging and dying incarcerated persons (i.e. cognitive measure) and attitudes, motivations and values for providing care (i.e. affective measure). Cognitive and affective post-tests were significantly better than at pre-test (all ps < 0.01). ANCOVAs revealed no significant differences for sex or ethnicity. ORIGINALITY/VALUE: Outcomes reveal that the CBL is acceptable, feasible and usable in corrections. Staff improved their knowledge after receiving the training. Correctional settings face increasing pressures to better address the health care and management needs of aged, chronically ill and dying incarcerated persons. This e-learning holds promise to contribute to better preparation of corrections staff to effectively care for these populations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.