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Record W4402794760 · doi:10.2196/preprints.58629

Nursing Staff’s Perspectives of Care Robots for Assisted Living Facilities: Systematic Literature Review (Preprint)

2024· preprint· en· W4402794760 on OpenAlexaboutno aff
Katie Trainum, Jiaying Liu, Elliott Hauser, Bo Xie

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintNursingRobotPeer reviewPsychologyMedicineComputer sciencePolitical scienceWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND Care robots have been proposed in response to nursing shortages in assisted living facilities (ALFs) and the growing population of older adults. While the use of care robots may improve the general health and well-being of older adults, their introduction changes the work of nursing staff fundamentally, and it has implications for the entire health care system. In developing such technology, it is important to include end users, but so far, the nursing staff’s perspectives have largely been ignored. OBJECTIVE This study aims to examine the literature on nursing staff’s attitudes, needs, and preferences related to the use of care robots in ALFs, in order to discover gaps in the literature and guide future research. METHODS This review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 protocol. On May 12, 2023, we searched PubMed, CINAHL Plus with Full Text, PsycINFO, the IEEE Xplore Digital Library, and the ACM Digital Library using predetermined search terms. Included publications, written in English, focused on the predevelopment phase, in which information was gathered on nursing staff’s attitudes, needs, and preferences regarding care robots for ALFs. Publications were excluded if they did not provide peer-reviewed empirical data. The studies’ findings were summarized, coded, and analyzed into major themes using thematic analysis and narrative synthesis. Their quality was assessed using McGill University’s Mixed Methods Appraisal Tool and the Joanna Briggs Institute’s critical appraisal tools. RESULTS The final sample included 15 studies. Most of the studies (n=11, 73%) were rated as good quality; however, there was a general lack of reporting on important methodological decisions and sample characteristics. Nursing staff desired care robots that could assist with physically demanding tasks and reduce their workload but had mixed feelings on whether robots could or should assist with social tasks. In addition, nursing staff are concerned about the ethics of care robots, as well as about their safety, accessibility, and operability. The nursing staff’s culture, qualification, and role in the facility may influence their perspectives of care robots. The studies lacked theory-driven designs and large sample sizes. Eight (53%) studies mentioned using a participatory design approach, but a lack of established criteria for what constitutes participatory design leads to varying degrees of methodological quality. CONCLUSIONS There was consensus among nursing staff that care robots should serve as nursing assistants to reduce workload. Whether robots could or should assist with social tasks remains a question. Further research is needed to mitigate nursing staff’s concerns and understand the socioecological factors that influence their perspectives of care robots and their adoption in ALFs. In addition, theory-driven and large sample size study designs are necessary, as well as work to develop clear criteria for related participatory design research.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewlow
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.090
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.090
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.007
Bibliometrics0.0150.016
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.043
GPT teacher head0.410
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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