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Record W3087972201 · doi:10.2196/20813

Investigating the Acceptance of Video Consultation by Patients in Rural Primary Care: Empirical Comparison of Preusers and Actual Users

2020· article· en· W3087972201 on OpenAlexvenueno aff
Marius Mueller, Michael Knop, Björn Niehaves, Charles Christian Adarkwah

Bibliographic record

VenueJMIR Medical Informatics · 2020
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsWorkloadVideoconferencingPerceptionNursingHealth carePopulationPrimary careMedicinePsychologyMedical educationFamily medicineComputer scienceMultimedia

Abstract

fetched live from OpenAlex

BACKGROUND: The ongoing digitalization in health care is enabling patients to receive treatment via telemedical technologies, such as video consultation (VC), which are increasingly being used by general practitioners. Rural areas in particular exhibit a rapidly aging population, with an increase in associated health issues, whereas the level of attraction for working in those regions is decreasing for young physicians. Integrating telemedical approaches in treating patients can help lessen the professional workload and counteract the trend toward the spatial undersupply in many countries. As a result, an increasing number of patients are being confronted with digital treatment and new forms of care delivery. These novel ways of care engender interactions with patients and their private lives in unprecedented ways, calling for studies that incorporate patient needs, expectations, and behavior into the design and application of telemedical technology within the field of primary care. OBJECTIVE: This study aims to unveil and compare the acceptance-promoting factors of patients without (preusers) and with experiences (actual users) in using VC in a primary care setting and to provide implications for the design, theory, and use of VC. METHODS: In total, 20 semistructured interviews were conducted with patients in 2 rural primary care practices to identify and analyze patient needs, perceptions, and experiences that facilitate the acceptance of VC technology and adoption behavior. Both preusers and actual users of VC were engaged, allowing for an empirical comparison. For data analysis, a procedure was followed based on open, axial, and selective coding. RESULTS: The study delivers factors and respective subdimensions that foster the perceptions of patients toward VC in rural primary care. Factors cover attitudes and expectations toward the use of VC, the patient-physician relationship and its impact on technology assessment and use, patients' rights and obligations that emerge with the introduction of VC in primary care, and the influence of social norms on the use of VC and vice versa. With regard to these factors, the results indicate differences between preusers and actual users of VC, which imply ways of designing and implementing VC concerning the respective user group. Actual users attach higher importance to the perceived benefits of VC and their responsibility to use it appropriately, which might be rooted in the technological intervention they experienced. On the contrary, preusers valued the opinions and expectations of their peers. CONCLUSIONS: The way the limitations and potential of VC are perceived varies across patients. When practicing VC in primary care, different aspects should be considered when dealing with preusers, such as maintaining a physical interaction with the physician or incorporating social cues. Once the digital intervention takes place, patients tend to value benefits such as flexibility and effectiveness over potential concerns.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.039
GPT teacher head0.373
Teacher spread0.334 · 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 designObservational
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

Citations40
Published2020
Admission routes1
Has abstractyes

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