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Record W3133933950 · doi:10.2196/25220

Preservation of Person-Centered Care Through Videoconferencing for Patient Follow-up During the COVID-19 Pandemic: Case Study of a Multidisciplinary Care Team

2021· article· en· W3133933950 on OpenAlexvenueno aff
Line Silsand, Gro-Hilde Severinsen, Gro Berntsen

Bibliographic record

VenueJMIR Formative Research · 2021
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsVideoconferencingTelehealthMultidisciplinary approachHealth careNursingFocus groupTelemedicinePandemicPactMedicinePsychologyCoronavirus disease 2019 (COVID-19)BusinessPolitical scienceMultimediaComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: The Patient-Centered Team (PACT) focuses on the transitional phase between hospital and primary care for older patients in Northern Norway with complex and long-term needs. PACT emphasizes a person-centered care approach whereby the sharing of power and the patient's response to "What matters to you?" drive care decisions. However, during the COVID-19 pandemic, videoconferencing was the only option for assessing, planning, coordinating, and performing treatment and care. OBJECTIVE: The aim of this study is to report the experience of the PACT multidisciplinary health care team in shifting rapidly from face-to-face care to using videoconferencing for clinical and collaborative services during the initial phase of the COVID-19 pandemic. This study explores how PACT managed to maintain person-centered care under these conditions. METHODS: This case study takes a qualitative approach based on four semistructured focus group interviews carried out in May and June 2020 with 19 PACT members and leaders. RESULTS: The case study illustrates that videoconferencing is a good solution for many persons with complex and long-term needs and generates new opportunities for interaction between patients and health care personnel. Persons with complex and long-term needs are a heterogeneous group, and for many patients with reduced cognitive capacity or hearing and vision impairment, the use of videoconferencing was challenging and required support from relatives or health care personnel. The study shows that using videoconferencing offered an opportunity to use health care personnel more efficiently, reduce travelling time for patients, and improve the information exchange between health care levels. This suggests that the integration of videoconferencing contributed to the preservation of the person-centered focus on care during the COVID-19 pandemic. There was an overall agreement in PACT that face-to-face care needed to be at the core of the person-centered care approach; the main use of videoconferencing was to support follow-up and coordination. CONCLUSIONS: The COVID-19 pandemic and the rapid adoption of digital care have generated a unique opportunity to continue developing a health service to both preserve and improve the person-centered care approach for persons with complex and long-term needs. This creates demand for overall agreements, including guidelines and procedures for how and when to use videoconferencing to supplement face-to-face treatment and care. Implementing videoconferencing in clinical practice generates a need for systematic training and familiarization with the equipment and technology as well as for an extensive support organization. Videoconferencing can then contribute to better preparing health care services for future scenarios.

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)0.007
metaresearch head score (Gemma)0.019
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 score0.018
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0180.006
Scholarly communication0.0040.003
Open science0.0030.006
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.275
GPT teacher head0.503
Teacher spread0.228 · 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

Citations22
Published2021
Admission routes1
Has abstractyes

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