Colour Vision Deficiencies in the Digital Age: A Survey of User Experiences with Digital Displays
Notice bibliographique
Résumé
Introduction: Approximately 90% of dichromats and 67% of anomalous trichromats experience difficulty performing colour-related tasks daily (Steward & Cole, 1989). As technology becomes more prevalent, examining whether this condition impacts their use of digital displays is increasingly essential. Limited studies analyze the relationship between colour vision deficiencies (CVD) and digital displays. Mashige (2019) found that 63.2% of schoolchildren with CVD reported challenges when “working with computers,” suggesting that this condition may have some effect. \nPurpose: The purpose of this study is to examine the impact that congenital red-green CVD has on an individual’s interaction with digital displays in different areas of their daily life, such as “school,” “work/volunteering,” “gaming and eSports,” “driving/motorized vehicle,” and “travelling.” \nMaterials and Method: An online survey was administered to people with CVD from Canada and the United States of America (USA). In addition to collecting demographic information and awareness of their CVD, information regarding difficulties carrying out colour-related tasks was collected. Because several software and filter options may improve performance on colour-related tasks, we asked whether they tried any and to rate their effectiveness. \nResults: A total of 381 individuals with CVD (280 males) completed the survey. Nearly 100% reported some difficulty with one of the tasks in most areas of life, individually, except for travelling (54.1%). For most tasks, there were no significant differences in the rankings based on sex and/or age group. Still, some tasks showed significant differences based on severity, primarily between mild and severe defects. Some examples included “colour-coded diagrams” at school, work/volunteering, and recreation/hobbies; “editing photos or other coloured images” at school, recreation/hobbies, and social media; and “reading coloured letters on various backgrounds” in gaming/eSports, online shopping/banking, and in-person shopping/banking. \nNearly 65% of respondents reported making changes or implementing modifications to their displays, but there were 35% who did not. The most popular aid was “trial-and-error adjustments” of the colour and brightness of the display. Of the individuals who tried an aid, approximately 90% reported a modification to be at least a little effective, less than 3% reported at least one aid as ineffective, and only 25% rated the modifications as highly effective. There was no significant difference based on sex, age group, or severity for most modifications, except for passive aids (“coloured filters”) where youth found them more effective than adults. \n“Desktops/laptops,” “cell phones,” and “tablets” were the displays that people with CVD most frequently encountered difficulty and modified. The displays showed significant association with age group for some areas of life (work/volunteering, recreation/hobbies, gaming & eSports, and online shopping/banking) and modifications. This was probably due to the greater use of displays or differences in content, with youth having more problems with tablets and adults more with cell phones. \nConclusion: Most respondents encountered some difficulty with at least one task, indicating that CVD had some effect on their ability to use digital displays. Although most respondents have tried some modifications to their displays, some respondents reported that at least one modification was ineffective and only 25% found an aid to be highly effective. Software developers should focus on making aids more accommodating and customizable to individual preferences.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».