Using Gaze Tracking as a Research Tool in the Deaf Health Literacy and Access to Health Information Project: Protocol for a Multisite Mixed Methods Study and Preliminary Results
Notice bibliographique
Résumé
BACKGROUND: Previous studies have identified the internet as a major source of health information. Reliable and accessible sources of web-based health information are critical for cultivating patient-centered care. However, the accessibility and use of web-based health information remains largely unknown for deaf individuals. We used gaze-tracking technology to understand the navigation and use of web-based health information by deaf adults who communicate with sign language and by hearing adults. OBJECTIVE: This paper discusses our protocol for implementing gaze-tracking technology in a study that included both deaf and hearing participants. We report the preliminary results and lessons learned from the implementation of the protocol. METHODS: We conducted gaze-tracking sessions with 450 deaf signers and 450 hearing participants as a part of a larger, multisite mixed methods research study. Then, we conducted qualitative elicitation interviews with a subsample of 21 deaf and 13 hearing participants, who engaged in a search task and reviewed their gaze recordings. To our knowledge, no study has implemented a similar research protocol to better understand the experiences of deaf adults. As such, we also examined research staff notes and observations from team meetings regarding the conduct of gaze-tracking data to delineate lessons learned and best practices for research protocols in this area. RESULTS: Findings from the implementation of this study protocol highlight the use of gaze technology with deaf participants. We developed additional protocol steps to minimize gaze disruption from either lipreading or communicating in sign language. For example, research assistants were often unable to maintain eye contact with participants while signing because of the need to simultaneously point at the computer monitor to provide instructions related to gaze study components, such as the calibration process. In addition to developing ways to effectively provide instructions in American Sign Language, a practice exercise was included in the gaze tracker session to familiarize participants with the computer and technology. The use of the playback feature permitted a deeper dialogue between researchers and participants, which we found vital for understanding the experiences of deaf participants. CONCLUSIONS: On the basis of our experience using the study protocol through a large research project, incorporating gaze-tracking technology offers beneficial avenues for better understanding how individuals interact with health information. Gaze tracking can determine the type and placement of visual content that attracts attention from the viewers of diverse backgrounds, including deaf individuals. The lessons learned through this study will help future researchers in determining ideal study designs, such as suitable protocols and participant characteristics (eg, deaf signers), while including gaze trackers in their projects. This approach explored how different ways of presenting health information can affect or enable visual learners to engage and use health information effectively. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/26708.
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 | 0,163 | 0,153 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,005 |
| Méta-épidémiologie (sens large) | 0,006 | 0,004 |
| Bibliométrie | 0,006 | 0,005 |
| Études des sciences et des technologies | 0,009 | 0,005 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,005 | 0,005 |
| Intégrité de la recherche | 0,009 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,043 | 0,012 |
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 ».