Web design and implementation factors associated with missed opportunities to provide testing on GetCheckedOnline, British Columbia’s digital testing service for sexually transmitted and blood-borne infections: 2022 client experience survey findings
Notice bibliographique
Résumé
Abstract Background We assessed associations between web-design/implementation factors and missed opportunities to provide testing via GetCheckedOnline and assessed if these associations were modified by sociodemographic factors. Methods A cross-sectional survey was conducted in November and December 2022 among clients who indicated needing testing when they created accounts between April and October 2022. Web-design (user interface and experience) and implementation (organization of clinical services around the website) factors were independently modelled against missed opportunities (self-reported inability/unwillingness to test despite needing testing at account creation) using multivariable logistic regression. Effect modification by sociodemographic factors were also conducted. Results Among 572 respondents needing testing at account creation, 183 (32.0%, 95%CI: 28.18-35.99%) experienced missed opportunities. Web-design factors associated with missed opportunities were difficulty using GetCheckedOnline’s website (adjusted odds ratio (aOR) 3.40, 95%CI:1.68-6.87), while implementation factors were difficulty getting to a laboratory (aOR:3.26, 95%CI:1.97-5.41); perceived inadequacy of tests offered through GetCheckedOnline (aOR:1.81, 95%CI:1.11-2.95) and being likely to complete testing if self-sampling was available (aOR:2.12, 95%CI:1.32-3.42). Findings were consistent in sensitivity analyses but concerns about privacy and security of personal information on GetCheckedOnline (aOR:1.93, 95%CI:1.11-3.35) was associated with missed opportunities. Sociodemographic factors modified associations as respondents with annual income < $20,000CAD, not employed full-time, immigrants, men (who did not agree GetCheckedOnline offered all needed tests) and women (who experienced difficulties getting to a laboratory) had higher odds missed opportunities. Conclusions Simplifying web-design, ensuring optimal client education, and including more laboratory locations and self-sampling as options for testing, could reduce missed opportunities and promote equitable access to GetCheckedOnline. Author Summary Digital health interventions like GetCheckedOnline aim to improve access to testing for sexually transmitted and blood-borne infections (STBBIs), but barriers related to web design and service implementation can limit their impact. Our study, based on a 2022 client experience survey, examined how these factors contribute to missed opportunities for testing among GetCheckedOnline users in British Columbia, Canada. We found that 32% of users who created accounts intending to test, reported not testing through the service. They reported barriers including difficulty navigating the website, accessing laboratories, and concerns about the adequacy of available tests. Importantly, these barriers varied across sociodemographic groups, with individuals with lower incomes, immigrants, and women facing the greatest challenges. Our findings suggest that simplifying website navigation, expanding laboratory access, and introducing self-sampling options could reduce missed opportunities and improve equitable access to digital STBBI testing. These insights highlight the need for ongoing, data-driven optimizations to ensure digital health services effectively reach those who face the greatest barriers to care.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».