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Record W4385798399 · doi:10.2196/43237

Experiences With In-Person and Virtual Health Care Services for People With Chronic Obstructive Pulmonary Disease: Qualitative Study

2023· article· en· W4385798399 on OpenAlexvenueno aff
Thea Krag, Emma Højgaard Jørgensen, Klaus Phanareth, Lars Kayser

Bibliographic record

VenueJMIR Rehabilitation and Assistive Technologies · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsnot available
Fundersnot available
KeywordsPulmonary diseaseQualitative researchMedicineHealth careDiseasePsychologyIntensive care medicineInternal medicineSociologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The World Health Organization and the European Commission predict increased use of health technologies in the future care for patients in Europe. Studies have shown that services based on telehealth, which includes components of education, as well as rehabilitation initiatives can support the self-management of individuals living with COPD. This raises an interest in how virtual and in-person interactions and roles can best be organized in a way that suits people living with COPD in relation to their treatment and rehabilitation. OBJECTIVE: This study aims to investigate how individuals living with COPD experience different combinations of virtual and in-person care, to help us better understand what aspects are valued and how to best combine elements of these services in future care. METHODS: Two rounds of semistructured interviews were conducted with 13 and 4 informants, respectively. The individuals were all recruited in relation to a research project led by the telehealth initiative Epital Health. The first round of interviews included 11 informants, as 2 dropped out. Of these, 7 received the telemedicine service provided by Epital Health, 3 participated in a 12-week COPD program provided by their respective municipality, and 1 did not receive any supplementary service besides the usual care. In the second round, which included 4 informants, all had at one point received the telemedicine service and participated in a municipality-based rehabilitation program. A content analysis of the interviews was performed based on deductive coding with 4 categories, namely, (1) Self-management, (2) Health-related support, (3) Digital context, and (4) Well-being. RESULTS: Medical and emotional support from health care professionals is a key aspect of care for individuals with COPD. Acute treatment with at-home medicine, monitoring one's own condition through technology, and having easy access and close contact with health care professionals familiar to them can promote self-management and well-being, as well as provide a feeling of security. Having regular meetings with a network of peers and health care professionals provides education, support, and tools to cope with the condition and improve own health. Furthermore, group-based activity motivates and increases the activity level of the individuals. Continued offers of services are desired as many experience a decrease in achieved benefits after the service ends. More emphasis is placed on the importance of the therapeutic and medical elements of care compared with factors such as technology. The identified barriers related to optimal utilization of the virtual service were related to differentiation in levels of contact depending on disease severity and skills related to the practical use of equipment. CONCLUSIONS: A combination of virtual and in-person services providing lasting medical and social support is suggested for the future. This should build upon the preferences and needs of individuals living with COPD and support relationships to caregivers and peers.

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)0.010
metaresearch head score (Gemma)0.011
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0100.008
Scholarly communication0.0040.004
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.348
Teacher spread0.332 · 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
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

Citations9
Published2023
Admission routes1
Has abstractyes

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