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Record W4365813919 · doi:10.2196/46673

Usefulness of a Digitally Assisted Person-Centered Care Intervention: Qualitative Study of Patients’ and Nurses’ Experiences in a Long-term Perspective

2023· article· en· W4365813919 on OpenAlexvenueno aff
Mette Linnet Olesen, Sine Rossen, Rikke Jørgensen, Line Langballe Udbjørg, Helena Hansson

Bibliographic record

VenueJMIR Nursing · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Intervention (counseling)Term (time)Qualitative researchNursingPsychologyMedicineLong-term careSociologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Person-centered care responsive to individual preferences, needs, and values is recognized as an important aspect of high-quality health care, and patient empowerment is increasingly viewed as a central core value of person-centered care. Web-based interventions aimed at empowerment report a beneficial effect on patient empowerment and physical activity; however, there is limited information available on barriers, facilitators, and user experiences. A recent review of the effect of digital self-management support tools suggests a beneficial effect on the quality of life in patients with cancer. On the basis of an overall philosophy of empowerment, guided self-determination is a person-centered intervention that uses preparatory reflection sheets to help achieve focused communication between patients and nurses. The intervention was adapted into a digital version called digitally assisted guided self-determination (DA-GSD) hosted by the Sundhed DK website that can be delivered face-to-face, via video, or by the combination of the 2 methods. OBJECTIVE: We aimed to investigate the experiences of nurses, nurse managers, and patients of using DA-GSD in 2 oncology departments and 1 gynecology department over a 5-year implementation period from 2018 to 2022. METHODS: This qualitative study was inspired by action research comprising the responses of 17 patients to an open-ended question on their experience of specific aspects of DA-GSD in a web questionnaire, 14 qualitative semistructured interviews with nurses and patients who initially completed the web questionnaire, and transcripts of meetings held between the researchers and nurses during the implementation of the intervention. The thematic analysis of all data was done using NVivo (QSR International). RESULTS: The analysis generated 2 main themes and 7 subthemes that reflect conflicting perspectives and greater acceptability of the intervention among the nurses over time owing to better familiarity with the increasingly mature technology. The first theme was the different experiences and perspectives of nurses and patients concerning barriers to using DA-GSD and comprised 4 subthemes: conflicting perspectives on the ability of patients to engage with DA-GSD and how to provide it, conflicting perspectives on DA-GSD as a threat to the nurse-patient relationship, functionality of DA-GSD and available technical equipment, and data security. The other theme was what influenced the increased acceptability of DA-GSD among the nurses over time and comprised 3 subthemes: a re-evaluation of the nurse-patient relationship; improved functionality of DA-GSD; and supervision, experience, patient feedback, and a global pandemic. CONCLUSIONS: The nurses experienced more barriers to DA-GSD than the patients did. Acceptance of the intervention increased over time among the nurses in keeping with the intervention's improved functionality, additional guidance, and positive experiences, combined with patients finding it useful. Our findings emphasize the importance of supporting and training nurses if new technologies are to be implemented successfully.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.069
GPT teacher head0.399
Teacher spread0.330 · 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 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

Citations10
Published2023
Admission routes1
Has abstractyes

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