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Record W2898949741 · doi:10.2196/11278

Co-Designing an eHealth Service for the Co-Care of Parkinson Disease: Explorative Study of Values and Challenges

2018· article· en· W2898949741 on OpenAlexvenueno aff
Åsa Revenäs, Helena Hvitfeldt Forsberg, Emma Granström, Carolina Wannheden

Bibliographic record

VenueJMIR Research Protocols · 2018
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordseHealthHealth careNursingDigital healthService (business)MedicinePsychologyMedical educationBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: The need for services to support patient self-care and patient education has been emphasized for patients with chronic conditions. People with chronic conditions may spend many hours per year in health and social care services, but the majority of time is spent in self-care. This has implications in how health care is best organized. The term co-care specifically stresses the combination of health care professionals' and patients' resources, supported by appropriate (digital) tools for information exchange, to achieve the best possible health outcomes for patients. Developers of electronic health (eHealth) services need to consider both parties' specific needs for the service to be successful. Research on participants' experiences of participating in co-design sessions is scarce. OBJECTIVE: The aim of this study was to describe different stakeholders' (people with chronic conditions, health care professionals, and facilitators) overall experiences of participating in co-design workshops aimed at designing an eHealth service for co-care for Parkinson disease, with a particular focus on the perceptions of values and challenges of co-design as well as improvement suggestions. METHODS: We conducted 4 half-day co-design workshops with 7 people with Parkinson disease and 9 health care professionals. Data were collected during the workshop series using formative evaluations with participants and facilitators after each workshop, researchers' diary notes throughout the co-design process, and a Web-based questionnaire after the final workshop. Quantitative data from the questionnaire were analyzed using descriptive statistics. Qualitative data were triangulated and analyzed inductively using qualitative content analysis. RESULTS: Quantitative ratings showed that most participants had a positive general experience of the co-design workshops. Qualitative analysis resulted in 6 categories and 30 subcategories describing respondents' perceptions of the values and challenges of co-design and their improvement suggestions. The categories concerned (1) desire for more stakeholder variation; (2) imbalance in the collaboration between stakeholders; (3) time investment and commitment paradox; (4) desire for both flexibility and guidance; (5) relevant workshop content, but concerns about goal achievement; and (6) hopes and doubts about future care. CONCLUSIONS: Based on the identified values and challenges, some paradoxical experiences were revealed, including (1) a desire to involve more stakeholders in co-design, while preferring to work in separate groups; (2) a desire for more preparation and discussions, while the required time investment was a concern; and (3) the experience that co-design is valuable for improving care, while there are doubts about the realization of co-care in practice. The value of co-design is not mainly about creating new services; it is about improving current practices to shape better care. The choice of methods needs to be adjusted to the stakeholder group and context, which will influence how they experience the process and outcomes of co-design.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0080.008
Scholarly communication0.0040.005
Open science0.0030.009
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.665
GPT teacher head0.679
Teacher spread0.014 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreProtocol

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

Citations36
Published2018
Admission routes1
Has abstractyes

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