MétaCan
Menu
← Back to cohort
Record W4283165629 · doi:10.2196/38263

COVID-box Experiences of Patients and Health Care Professionals (COVID-box Project): Single-Center, Retrospective, Observational Study

2022· article· en· W4283165629 on OpenAlexvenueno aff
Ebru Dirikgil, Kim Brons, Michael Duindam, Geert H. Groeneveld, J.J. Miranda Geelhoed, Christian Heringhaus, Paul J. M. van der Boog, Ton J. Rabelink, Willem Jan W. Bos, Niels H. Chavannes, Douwe E. Atsma, Y K Onno Teng

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsLikert scaleObservational studyCoronavirus disease 2019 (COVID-19)MedicineHealth careRetrospective cohort studyScale (ratio)Family medicineNursingPatient satisfactionHealth professionalsPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: During the COVID-19 pandemic, several home monitoring programs have described the success of reducing hospital admissions, but only a few studies have investigated the experiences of patients and health care professionals. OBJECTIVE: The objective of our study was to determine patients' and health care professionals' experiences and satisfaction with employing the COVID-box. METHODS: In this single-center, retrospective, observational study, patients and health care professionals were asked to anonymously fill out multiple-choice questionnaires with questions on a 5-point or 10-point Likert scale. The themes addressed by patients were the sense of reassurance and safety, experiences with teleconsultations, their appreciation for staying at home, and the instructions for using the COVID-box. The themes addressed by health care professionals who treated patients with the COVID-box were the characteristics of the COVID-box, the technical support service and general satisfaction, and their expectations and support for this telemonitoring concept. Scores were interpreted as insufficient (≤2 or ≤5, respectively), sufficient (3 or 6-7, respectively), or good (≥4 or ≥8, respectively) on a 5-point or 10-point Likert scale. RESULTS: A total of 117 patients and 25 health care professionals filled out the questionnaires. The median score was 4 (IQR 4-5) for the sense of safety, the appreciation for staying at home, and experiences with teleconsultations, with good scores from 76.5% (88/115), 86% (56/65), and 83.6% (92/110) of the patients, respectively. Further, 74.4% (87/117) of the patients scored the home monitoring program with a score of ≥8. Health care professionals scored the COVID-box with a minimum median score of 7 (IQR 7-10) on a 10-point scale for all domains (ie, the characteristics of the COVID-box and the technical support service and general satisfaction). For the sense of safety, user-friendliness, and additional value of the COVID-box, the median scores were 8 (IQR 8-10), 8 (IQR 7-9), and 10 (IQR 8-10), respectively, with good scores from 86% (19/22), 75% (15/20), and 96% (24/25) of the health care professionals, respectively. All health care professionals (25/25, 100%) gave a score of ≥8 for supporting this home monitoring concept, with a median score of 10 (IQR 10-10). CONCLUSIONS: The positive experiences and satisfaction of involved users are key factors for the successful implementation of a novel eHealth solution. In our study, patients, as well as health care professionals, were highly satisfied with the use of the home monitoring program-the COVID-box project. Remote home monitoring may be an effective approach in cases of increased demand for hospital care and high pressure on health care systems.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.371
GPT teacher head0.578
Teacher spread0.207 · 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 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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Formative Research→Same topicCOVID-19 and Mental Health→French-language works237,207→