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Record W4385320669 · doi:10.2196/48670

A Web-Based, Mail-Order Sexually Transmitted Infection Testing Program: Qualitative Analysis of User Feedback

2023· article· en· W4385320669 on OpenAlexvenueno aff
Abagail Edwards, A. Ugarte Nuño, Christopher G. Kemp, Emily Tillett, Gretchen Armington, Rachel Fink, Matthew M. Hamill, Yukari C. Manabe

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive tract infections research
Canadian institutionsnot available
Fundersnot available
KeywordsGonorrheaConfidentialityChlamydiaMedicineThematic analysisFamily medicineTrichomoniasisGynecologyComputer scienceQualitative researchComputer securityHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

BACKGROUND: The incidence of sexually transmitted infections (STIs) is increasing in the United States. The COVID-19 pandemic resulted in significant reductions in access to health care services, including STI testing and treatment, leading to underreporting of STI cases and a need for alternatives to clinic-based testing. Moreover, concerns around confidentiality, accessibility, and stigma continue to limit access to clinic-based STI testing, particularly for high-priority populations. IWantTheKit (IWTK) is a web-based platform that mails free, confidential, self-administered sample collection kits for testing for gonorrhea, chlamydia (both genital and extragenital sites), and vaginal trichomonas. Individuals visiting the IWTK website may select genital, pharyngeal, and rectal samples for chlamydia and gonorrhea testing. Vaginal samples are tested for trichomoniasis. Self-collected samples are processed in a College of American Pathologists-accredited laboratory, and results are posted to an individual's secure digital account. OBJECTIVE: This study aimed to (1) describe users' experience with the IWTK service through analysis of routine data and (2) optimize retention among current users and expand reach among high-priority populations by responding to user needs through programmatic and functional changes to the IWTK service. METHODS: Free-text entries were submitted by IWTK users via a confidential "Contact Us" page on the IWTK website from May 17, 2021, to January 31, 2022. All entries were deidentified prior to analysis. Two independent analysts coded these entries using a predefined codebook developed inductively for thematic analysis. RESULTS: A total of 254 free-text entries were analyzed after removing duplicates and nonsensical entries. Themes emerged regarding the functionality of the website and personal experiences using IWTK's services. Users' submissions included requests related to order status, address changes, replacement of old kits, clinical information (eg, treatment options and symptom reports), and reported risk behaviors. CONCLUSIONS: This analysis demonstrates how routine data can be used to propose potential programmatic improvements. IWTK implemented innovations on the website based on the study results to improve users' experience, including a tracking system for orders, address verification for each order, a physical drop box, additional textual information, direct linkage to care navigation, and printable results. Web-based, mail-order STI testing programs can leverage user feedback to optimize implementation and retention among current users and potentially expand reach among high-priority populations. This analysis is supported by other data that demonstrate how comprehensive support and follow-up care for individuals testing positive are critical components of any self-testing service. Additional formal assessments of the IWTK user experience and efforts to optimize posttesting linkage to care may be needed.

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)0.021
metaresearch head score (Gemma)0.040
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 score0.021
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0010.004
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 head0.102
GPT teacher head0.469
Teacher spread0.367 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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