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Record W3207586487 · doi:10.2196/33088

Assessing Values and Preferences Toward SARS-CoV-2 Self-testing Among the General Population and Their Representatives, Health Care Personnel, and Decision-Makers: Protocol for a Multicountry Mixed Methods Study

2021· article· en· W3207586487 on OpenAlexvenueno aff
Sonjelle Shilton, Elena Ivanova Reipold, Albert Roca Álvarez, Guillermo Z. Martínez‐Pérez

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsnot available
FundersDurban University of Technology
KeywordsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Protocol (science)Coronavirus disease 2019 (COVID-19)Population2019-20 coronavirus outbreakHealth careMedicinePsychologyFamily medicineVirologyEnvironmental healthAlternative medicineDiseasePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Accessible, safe, and client-centered SARS-CoV-2 testing services are an effective way to halt its transmission. Testing enables infected individuals to isolate or quarantine to prevent further transmission. In countries with limited health systems and laboratory capacity, it can be challenging to provide accessible and safe screening for COVID-19. Self-testing provides a convenient, private, and safe testing option; however, it also raises important concerns about lack of counseling and ensuring timely reporting of self-test results to national surveillance systems. Investigating community members' views and perceptions regarding SARS-CoV-2 self-testing is crucial to inform the most effective and safe strategies for implementing said testing. OBJECTIVE: We aimed to determine whether SARS-CoV-2 self-testing was useful to diagnose and prevent the spread of SARS-CoV-2 for populations in low-resource settings and under which circumstances it would be acceptable. METHODS: This multisite, mixed methods, observational study will be conducted in 9 countries-Brazil, India, Indonesia, Kenya, Malawi, Nigeria, Peru, the Philippines, and South Africa-and will consists of 2 components: cross-sectional surveys and interviews (semistructured and group) among 4 respondent groupings: the general population, general population representatives, health care workers, and decision-makers. General population and health care worker survey responses will be analyzed separately from each other, using bivariate and multivariate inferential analysis and descriptive statistics. Semistructured interviews and group interviews will be audiorecorded, transcribed, and coded for thematic comparative analysis. RESULTS: As of November 19, 2021, participant enrollment is ongoing; 4364 participants have been enrolled in the general population survey, and 2233 participants have been enrolled in the health care workers survey. In the qualitative inquiry, 298 participants have been enrolled. We plan to complete data collection by December 31, 2021 and publish results in 2022 via publications, presentations at conferences, and dissemination events specifically targeted at local decision-makers, civil society, and patient groups. CONCLUSIONS: The views and perceptions of local populations are crucial in the discussion of the safest strategies for implementing SARS-CoV-2 self-testing. We intend to identify sociocultural specificities that may hinder or accelerate the widespread utilization of SARS-CoV-2 self-testing. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/33088.

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)0.064
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.064
Threshold uncertainty score0.341

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0640.032
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0030.003
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0280.005

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 head0.447
GPT teacher head0.632
Teacher spread0.184 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations25
Published2021
Admission routes1
Has abstractyes

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