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Record W4390191542 · doi:10.1002/alz.082738

How do professionals working in the field of dementia care define quality of nursing home care and how do they believe warm technology contributes to this?

2023· article· en· W4390191542 on OpenAlexaboutno aff
Claire M. Bernaards, Lizan Westenbrink, Marijke de Vries, Celeste M. Coolen

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsDignityDementiaThematic analysisNursingSocial connectednessPsychologyNursing careQuality (philosophy)MedicineQualitative researchSociologySocial psychologyDisease

Abstract

fetched live from OpenAlex

Abstract Background Warm technology (WT) aims to improve quality of life of people living with dementia by supporting and enhancing human potential, social connectedness, dignity and self‐reliance. However, how care researchers and dementia care professionals think about the relationship between WT and quality of care (QOC) in nursing homes (NHs) is unknown. Also, there is a lack of international consensus on the definition of QOC in NHs. The goal of this project was to explore how care researchers and dementia care professionals define the concept QOC in NHs and how they believe WT contributes to this. The Tovertafel, a research based technology that activates nursing home residents with dementia by projecting interactive games on a surface, served as an example for WT in this study. Method Semi‐structured (online) interviews were held in England, Ireland, Canada, United States of America and the Netherlands with 9 researchers and 11 care managers, 6 activity coordinators and 3 specialized nurses working in dementia care units of NHs. All care professionals had access to a Tovertafel. The interview guide was based on three existing multidimensional quality of nursing home care frameworks. To analyze the interviews, a thematic analysis was employed by means of open, axial, and selective coding using ATLAS.Ti. Results Most research and care professionals made a clear distinction between the clinical domains of QOC (e.g. providing effective and safe care) and the social domains of QOC (e.g. building relationships, trust, and providing person centered care) in NHs. According to the interviewees, the Tovertafel contributes to several social domains of QOC by: 1. supporting care professionals in the provision of person centered care and gaining trust from residents, 2. improving work pleasure of dementia care professionals, 3. creating a safe, pleasant and homelike environment, 4. increasing family involvement, and 5. increasing (non‐verbal) communication. The interviewees reported fewer and more indirect effects on the clinical domains of QOC in NHs. The results were comparable between countries. Conclusion Warm technology, such as the Tovertafel, seems to have a positive effect on QOC in NHs, primarily by having a positive impact on several social QOC domains.

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 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)0.039
metaresearch head score (Gemma)0.121
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.121
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.010
Scholarly communication0.0100.009
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.060
GPT teacher head0.408
Teacher spread0.348 · 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

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations0
Published2023
Admission routes1
Has abstractyes

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