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Record W4396899822 · doi:10.2196/58501

Improving the Acceptability and Implementation of Information and Communication Technology–Based Health Care Platforms for Older People With Dementia or Parkinson Disease: Qualitative Study Results of Key Stakeholders

2024· article· en· W4396899822 on OpenAlexvenueno aff
Mona Ahmed, Mayca Marín, Pilar Gangas, Ellen Bentlage, Claudia Louro, Michael Brach

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
FundersEuropean Commission
KeywordsInformation and Communications TechnologyHealth careKnowledge managementeHealthBusinessIntegrated careQualitative researchPreprintPublic relationsNursingMedicinePolitical scienceSociologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: The management of neurodegenerative diseases (NDDs) in older populations is usually demanding and involves care provision by various health care services, resulting in a greater burden on health care systems in terms of costs and resources. The convergence of various health services within integrated health care models, which are enabled and adopted jointly with information and communication technologies (ICTs), has been identified as an effective alternative health care solution. However, its widespread implementation faces formidable challenges. Both the development and implementation of integrated ICTs are linked to the collaboration and acceptance of different groups of stakeholders beyond patients and health care professionals, with reported discrepancies in the needs and preferences among these groups. OBJECTIVE: Complementing a previous publication, which reported on the needs and requirements of end users in the development of the European Union-funded project PROCare4Life (Personalized Integrated Care Promoting Quality of Life for Older People), this paper aimed to report on the opinions of other key stakeholders from various fields, including academia, media, market, and decision making, for improving the acceptability and implementation of an integrated ICT-based health care platform supporting the management of NDDs. METHODS: The study included 30 individual semistructured interviews that took place between June and August 2020 in 5 European countries (Germany, Italy, Portugal, Romania, and Spain). Interviews were mostly conducted online, except in cases where participants requested to be interviewed in person. In these cases, COVID-19 PROCare4Life safety procedures were applied. RESULTS: This study identified 2 themes and 5 subthemes. User engagement, providing training and education, and the role played by the media were identified as strategic measures to ensure the acceptability of ICT-based health care platforms. Sustainable funding and cooperation with authorities were foreseen as additional points to be considered in the implementation process. CONCLUSIONS: The importance of the user-centered design approach in ensuring the involvement of users in the development of ICT-based platforms has been highlighted. The most common challenges that hinder the acceptability and implementation of ICT-based health care platforms can be addressed by creating synergies among the efforts of users, academic stakeholders, developers, policy makers, and decision makers. To support future projects in developing ICT-based health care platforms, this study outlined the following recommendations that can be integrated when conducting research on users' needs: (1) properly identify the particular challenges faced by future user groups without neglecting their social and clinical contexts; (2) iteratively assess the digital skills of future users and their acceptance of the proposed platform; (3) align the functionalities of the ICT platform with the real needs of future users; and (4) involve key stakeholders to guide the reflection on how to implement the platform in the future. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/22463.

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)0.000
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 score0.120
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.459
Teacher spread0.390 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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