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Record W2991938047 · doi:10.1177/1460458219889499

Perceptions and needs regarding technologies in nursing homes: An exploratory study

2019· article· en· W2991938047 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueHealth Informatics Journal · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversité LavalUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
FundersRéseau de recherche portant sur les interventions en sciences infirmières du Québec
KeywordsExploratory researchNursingPerceptionQualitative researchQuality (philosophy)Nursing homesMedicinePsychologySociology

Abstract

fetched live from OpenAlex

Two of the most salient problems in nursing homes are the responsive behaviours and falls of older people living with Alzheimer's disease and related disorders. Intelligent videomonitoring and mobile applications are potential technologies that may help prevent and manage these problems. However, evidence for the needs for technologies in nursing homes is scarce. This study aimed to explore the perceptions and needs of care managers, and of formal and family caregivers in nursing homes regarding these potential technologies. With an exploratory qualitative design based on Rogers' diffusion of innovation theory, individual interviews and a content analysis were conducted. Results show that the potential users of these technologies consider them relevant in nursing homes. The characteristics that would make these technologies useful in nursing homes are described. These results could be used to develop useful technologies to improve the quality of clinical practice in nursing homes.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.058
GPT teacher head0.444
Teacher spread0.387 · 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