Evidence map›Paper›PMID 33222218›Full record

ArticleJournal of advanced nursing2021

Mealtime verbal interactions among nursing home staff and residents with dementia: A secondary behavioural analysis of videotaped observations.

Wen Liu, Kristine Williams, Melissa Batchelor, Yelena Perkhounkova, Maria Hein

Abstract read
In one paragraph

Article in Journal of advanced nursing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Wen LiuCollege of Nursing, The University of Iowa, Iowa City, IA, USA.ORCID https://orcid.org/0000-0002-1491-7384
Kristine WilliamsSchool of Nursing, The University of Kansas, Kansas City, KS, USA.
Melissa BatchelorSchool of Nursing, George Washington University, Washington, DC, USA.
Yelena PerkhounkovaCollege of Nursing, The University of Iowa, Iowa City, IA, USA.
Maria HeinCollege of Nursing, The University of Iowa, Iowa City, IA, USA.

Funding

Scholarship and Mentoring CoreP30NR014139 · NINR · DUKE UNIVERSITY · PI BAILEY, DONALD E · 2012 to 2016
$2.3M
Changing Talk to Reduce Resistiveness to Dementia Care (CHAT)R01NR011455 · NINR · UNIVERSITY OF KANSAS MEDICAL CENTER · PI WILLIAMS, KRISTINE N. · 2010 to 2013
$1.5M
Optimizing Mealtime (OPTIMAL): Development and Pilot Testing of a Person-Centered Mealtime Care Intervention for Nursing Home Residents with Alzheimer's Disease and Related Dementias (ADRD)K23AG066856 · NIA · UNIVERSITY OF IOWA · PI LIU, WEN · 2020 to 2024
$778k
Person-Centered and Task-Centered Care in Temporal Relation to Mealtime Difficulties and Food Intake for Nursing Home Residents with DementiaR03AG063170 · NIA · UNIVERSITY OF IOWA · PI LIU, WEN · 2019 to 2020
$337k
American Nurses FoundationNational Institute of Aging at National Institute of Health R03AG063170National Institute of health/National Institute of Nursing Research 1P30 NR014139National Institute of health/National Institute of Nursing Research NR011455-04NIA NIH HHS K23 AG066856NIA NIH HHS R03 AG063170NINR NIH HHS P30 NR014139NINR NIH HHS R01 NR011455
6 · The paper itself

Abstract

aimsTo characterize dyadic mealtime verbal interactions and examine the associations with staff and resident characteristics.

designA secondary analysis of 110 videotaped mealtime observations collected from a dementia communication trial during 2011-2014.

methodsVideos involved 25 residents with dementia and 29 staff in nine nursing homes. Verbal behaviours (utterances) were coded during 2018-2019 using the Cue Utilization and Engagement in Dementia mealtime video-coding scheme, addressing eight positive behaviours and four negative behaviours. Bivariate analyses and multivariate regression models were used.

resultsStaff spoke three times more frequently (76.5%) than residents (23.5%). Nearly all staff utterances were positive (99.2%); 85.1% of residents' utterances were positive and 14.9% negative. Staff positive utterances were correlated with their negative utterances and resident positive and negative utterances. Staff negative utterances were correlated with resident negative utterances. Resident positive and negative utterances were correlated. Resident positive utterances were significantly associated with staff care-giving length in the current nursing home (OR = 1.430, 95% CI = 1.008, 2.027). Resident negative utterances were significantly associated with resident gender (female versus male, OR = 11.892, 95% CI = 1.237, 114.289) and staff years worked as a caregiver (OR = 0.838, 95% CI = 0.710, 0.989). Staff positive and negative utterances were not associated significantly with any participant characteristics.

conclusionsStaff engage residents using primarily positive verbal strategies. Staff-resident mealtime verbal interactions were dynamic, interactive, and complex and related to multiple individual characteristics. IMPACT: Positive dyadic mealtime interactions are critical to engage residents in eating. Little work has characterized dyadic mealtime interactions, limiting the development of effective interventions. Findings showed staff-resident mealtime verbal interactions were primarily positive, inter-related, and associated with multiple individual characteristics. Findings inform directions to improve mealtime care practice and develop person-centred mealtime interventions targeting modifiable factors, including staff care-giving experiences.

Indexed as

DementiaNursing StaffCaregiversFemaleHumansMaleMealsNursing Homesbehavioural codingcommunicationdementiadyadic interactionsmealtimenursingnursing homestaffverbalvideos

Identifiers

PMID33222218
PMCPMC7902310

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.