Listen, look, link and learn: a stepwise approach to use narrative quality data within resident-family-nursing staff triads in nursing homes for quality improvements

Sion, Katya; Rutten, Johanna E R; Hamers, Jan P.H.; Vries, Erica de; Zwakhalen, Sandra; Odekerken‐Schröder, Gaby; Schols, Jos M. G. A.; Verbeek, Hilde

doi:10.1136/bmjoq-2021-001434

Cited by 3 publications

(3 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All these experts have a minimum of ten years of experience in conducting qualitative research. A total of 103 transcripts were manually coded regarding the sentiment [24]. In this analysis, text segments were manually coded as being either 'positive' or 'negative'.…”

Section: Datamentioning

confidence: 99%

See 1 more Smart Citation

Comparing text mining and manual coding methods: Analysing interview data on quality of care in long-term care for older adults

Hacking,

Verbeek,

Hamers

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Objectives In long-term care for older adults, large amounts of text are collected relating to the quality of care, such as transcribed interviews. Researchers currently analyze textual data manually to gain insights, which is a time-consuming process. Text mining could provide a solution, as this methodology can be used to analyze large amounts of text automatically. This study aims to compare text mining to manual coding with regard to sentiment analysis and thematic content analysis. Methods Data were collected from interviews with residents (n = 21), family members (n = 20), and care professionals (n = 20). Text mining models were developed and compared to the manual approach. The results of the manual and text mining approaches were evaluated based on three criteria: accuracy, consistency, and expert feedback. Accuracy assessed the similarity between the two approaches, while consistency determined whether each individual approach found the same themes in similar text segments. Expert feedback served as a representation of the perceived correctness of the text mining approach. Results An accuracy analysis revealed that more than 80% of the text segments were assigned the same themes and sentiment using both text mining and manual approaches. Interviews coded with text mining demonstrated higher consistency compared to those coded manually. Expert feedback identified certain limitations in both the text mining and manual approaches. Conclusions and implications While these analyses highlighted the current limitations of text mining, they also exposed certain inconsistencies in manual analysis. This information suggests that text mining has the potential to be an effective and efficient tool for analysing large volumes of textual data in the context of long-term care for older adults.

show abstract

Section: Datamentioning

confidence: 99%

“…positive or negative. A positive or negative code was only assigned when it was perceived as being relevant to improving the quality of care [24].…”

Section: Text Mining Modelsmentioning

confidence: 99%

Comparing text mining and manual coding methods: Analysing interview data on quality of care in long-term care for older adults

Hacking,

Verbeek,

Hamers

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…AI is an upcoming approach in the field of nursing homes in the Netherlands. For example, Sion et al [ 22 ] advise using the 4D-cycle of AI for nursing staff to help them reflect on and learn from narrative qualitative data from the Connecting Conversations project, together with families and residents on an operational and tactical level. Sion et al [ 22 ] indicate that this use of the 4D-cycle of AI has yet to be investigated.…”

Section: Introductionmentioning

confidence: 99%

Learning and Developing Together for Improving the Quality of Care in a Nursing Home, Is Appreciative Inquiry the Key?

Graas

Gobbens

2023

Healthcare

View full text Add to dashboard Cite

To investigate the impact of Appreciative Inquiry (AI) on learning and developing together for improving the quality of care in a nursing home, and to explore experiences following the four phases of the AI cycle, an AI was performed as part of participative action research. Interviews, group discussions, creative methods of storytelling, and reflections were used in the AI sessions. Staff members were interviewed to evaluate the AI process. All of the interviews were recorded and transcribed. Data were analyzed thematically using Maxqda and were discussed by the interviewers until consensus was reached. The sessions resulted in ten action proposals to improve healthcare. One proposal was worked out in agreements on cooperation. The evaluation revealed that the sessions have given a boost to the team spirit, and involvement at the location leading to more cooperation and communication during the shifts. The evaluation indicated that it is important to convert the action plans of the sessions into actions, which are then evaluated. AI was advised as a way of learning where everyone is involved, and mutual agreements are made. This study has shown that AI can be a valuable way to support learning and development to promote the quality of care in a nursing home.

show abstract

Exploring nursing home resident and their care partner priorities for care using the Action-Project Method

et al. 2023

View full text Add to dashboard Cite

Background Nursing home (NH) residents’ experiences are embedded within their relationships to others. Our objectives were to describe how residents and care partners (family or staff members) jointly construct, discuss, and act on care priorities. Methods We used Action-Project Method, a qualitative method focused on action within social context. We recruited 15 residents and 12 care partners (5 family and 7 staff members) from 3 urban NHs in Alberta, Canada. Residents and care partners participated in a video-recorded conversation about their experiences in the NH, then individually reviewed the video-recording to add context to the conversation. Following transcription, preliminary narrative construction, and participant feedback, the research team conducted in-depth analysis to identify participant actions, goals, and projects, including those jointly shared by dyad members. Results All participants’ intentions could be broadly described as “making time in the NH as good as possible” and projects were grouped into five categories: resident identity, relationships (both presence and absence), advocacy, positivity, and respectful care. Participants often raised issues of short-staffing as a significant barrier to respectful care. Care partners, especially staff, used positivity to redirect residents from difficult topics. Joint projects could be identified in some, but not all, cases. Conclusions We found that maintaining a sense of identity, fostering relationships, and receiving respectful care were important to residents but that short-staffing created barriers. Methods to capture these aspects of the resident experience are needed but should not be influenced by care partners’ tendency towards positivity in resident interactions.

show abstract

Listen, look, link and learn: a stepwise approach to use narrative quality data within resident-family-nursing staff triads in nursing homes for quality improvements

Cited by 3 publications

References 37 publications

Comparing text mining and manual coding methods: Analysing interview data on quality of care in long-term care for older adults

Comparing text mining and manual coding methods: Analysing interview data on quality of care in long-term care for older adults

Learning and Developing Together for Improving the Quality of Care in a Nursing Home, Is Appreciative Inquiry the Key?

Exploring nursing home resident and their care partner priorities for care using the Action-Project Method

Contact Info

Product

Resources

About