MétaCan
Menu
Back to cohort
Record W2597271018 · doi:10.1177/1471301217698837

Improving Quality of Work life for Care Providers by Fostering the Emotional well-being of Persons with Dementia: A Cluster-randomized Trial of a Nursing Intervention in German long-term Care Settings

2017· article· en· W2597271018 on OpenAlexaff
Charlotte Berendonk, Roman Kaspar, Marion Bär, Matthias Hoben

Bibliographic record

VenueDementia · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Alberta
FundersBundesministerium für Bildung und Forschung
KeywordsDementiaIntervention (counseling)MedicineRandomized controlled trialNursingLong-term careQuality of life (healthcare)Cluster randomised controlled trialFamily medicine

Abstract

fetched live from OpenAlex

We tested the feasibility of a nursing intervention (DEMIAN) in routine care and its effects on care providers' job satisfaction, motivation, and work strain. This cluster-randomized trial was conducted in 20 German long-term care facilities. We randomly assigned 20 facilities to an intervention group (84 care providers, 42 residents with dementia) or a control group (96 care providers, 42 residents with dementia). Intervention group providers received two training days on the intervention; 68 providers attended both training days. Sixty two providers completed both baseline and follow-up questionnaires. Trained providers created individualized mini-intervention plans for participating residents. Control group residents received 'usual care'. Intervention group providers stated that the intervention was feasible and helped them improve emotional well-being of residents with dementia. We found significantly decreased time pressure and decreased job dissatisfaction for intervention group providers. DEMIAN is an effective and pragmatic contribution to implementing person-centred care in long-term care, with positive effects on providers' working conditions.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.396
Teacher spread0.362 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations17
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueDementiaSame topicGeriatric Care and Nursing HomesFrench-language works237,207