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Record W4393934894 · doi:10.2196/51237

Patient Engagement With and Perceptions of the COVIDA Project, a Volunteer-Led Telemonitoring and Teleorientation Service for COVID-19 Community Management: Mixed Methods Study

2024· article· en· W4393934894 on OpenAlexvenueno aff
Stefan Escobar-Agreda, Javier Silva‐Valencia, Percy Soto-Becerra, C. Mahony Reátegui-Rivera, Kelly De la Cruz-Torralva, Max Chahuara-Rojas, Bruno Hernandez-Iriarte, Daniel Hector Espinoza-Herrera, Carlos Delgado, Silvana M. Matassini Eyzaguirre, Javier Vargas-Herrera, Leonardo Rojas-Mezarina

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
FundersConsejo Nacional de Ciencia, Tecnología e Innovación TecnológicaUniversidad Nacional Mayor de San Marcos
KeywordsTelehealthService (business)PhoneMedicineTelemedicineIntervention (counseling)PerceptionCoronavirus disease 2019 (COVID-19)PsychologyVolunteerPandemicScheduleNursingFamily medicineHealth careDisease

Abstract

fetched live from OpenAlex

BACKGROUND: During the pandemic in Peru, the COVIDA (Collaboration Network of Volunteer Brigade Members for the Investigation, Detection, and Primary Management of Community Cases Affected by COVID-19) project proposed an innovative way to provide telemonitoring and teleorientation to COVID-19 patients, led by health care student volunteers. However, it has not been described how this interaction is perceived from the patient's perspective and which factors increase their engagement with this service. OBJECTIVE: The aim of this study is to describe the perceptions of patients about COVIDA and identify factors associated with their engagement with this service. METHODS: A mixed methods study was conducted to evaluate perceptions of patients that participated in the COVIDA project. This telehealth intervention organized by the National University of San Marcos was implemented in Peru from August to December 2020. The service involved daily phone calls by volunteer students to monitor registered COVID-19 patients until the completion of the 14th day of the illness or if a warning sign was identified. The volunteers also provided teleorientation to address the patients' needs and concerns. Quantitative analysis was performed to describe the characteristics of the patients and to assess the factors related to their engagement with the service, which was defined by the percentage of participants who completed the follow-up according to their individual schedule. Qualitative analysis through semistructured interviews evaluated the patients' perceptions of the service regarding the aspects of communication, interaction, and technology. RESULTS: Of the 770 patients enrolled in COVIDA, 422 (55.7%) were female; the median age was 39 (IQR 28-52) years. During the monitoring, 380 patients (49.4%) developed symptoms, and 471 (61.2%) showed warning signs of COVID-19. The overall median for engagement was 93% (IQR 35.7%-100%). Among those patients who did not develop warning signs, engagement was associated with the presence of symptoms (OR 3.04, 95% CI 2.22-4.17), a positive COVID-19 test at the start of follow-up (OR 1.97, 95% CI 1.48-2.61), and the presence of comorbidities (OR 1.83, 95% CI 1.29-2.59). Patients reported that the volunteers provided clear and valuable information and emotional support. Communication via phone calls took place smoothly and without interruptions. CONCLUSIONS: COVIDA represents a well-accepted and well-perceived alternative model for student volunteers to provide telemonitoring, teleorientation, and emotional support to patients with COVID-19 in the context of overwhelmed demand for health care services. The deployment of this kind of intervention should be prioritized among patients with symptoms and comorbidities, as they show more engagement with these services.

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)0.011
metaresearch head score (Gemma)0.013
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 score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.003
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 head0.128
GPT teacher head0.546
Teacher spread0.418 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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