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Record W4206100812 · doi:10.3928/19404921-20211209-02

Feasibility and Acceptability Testing of Evidence-Based Hydration Strategies for Residential Care

2022· article· en· W4206100812 on OpenAlexaff
Heather Keller, Cindy Wei, Ashwini Namasivayam‐MacDonald, Safura Syed, Christina Lengyel, Minn N. Yoon, Susan E. Slaughter, Phyllis Gaspar, George Heckman, Janet C. Mentes

Bibliographic record

VenueResearch in Gerontological Nursing · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsResearch Institute for Aging
Fundersnot available
KeywordsIntervention (counseling)NursingInclusion (mineral)StakeholderMedicineGerontological nursingNursing homesPsychologySocial psychologyPublic relations

Abstract

fetched live from OpenAlex

The current study examined stakeholder perspectives on the perceived effectiveness, feasibility, and acceptability of 20 evidence-based strategies appropriate for residential care via an online survey ( N = 162). Most participants worked in long-term care (83%), were direct care providers (62%), worked in food/nutrition roles (55%), and identified as female (94%). Strategies that were rated as effective, feasible, and likely to be used in the future were social drinking events, increased drink options at meals, and pre-thickened drinks. Participants also listed their top strategies for inclusion in a multicomponent intervention. Responses to open-ended questions provided insight on implementation, compliance, and budget constraints. Participant perspectives provide insight into developing a multicomponent intervention. Strategies prioritized for such an intervention include: staff education, social drinking opportunities, drinks trolley, volunteer support, improved beverage availability, hydration reminders, offering preferred beverages, and prompting residents to drink using various cues. [ Research in Gerontological Nursing, 15 (1), 27–38.]

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.673
GPT teacher head0.587
Teacher spread0.086 · 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.

Study designObservational
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

Citations2
Published2022
Admission routes1
Has abstractyes

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