MétaCan
Menu
Retour à la cohorte
Enregistrement W1579428508 · doi:10.18438/b8gw36

Mental Model Construction in MedlinePlus Information Searching Involves Changes and Developments in Cognition, Emotion, and Behaviour

2013· article· en· W1579428508 sur OpenAlexvenueno aff
Antonio P. DeRosa

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2013
Typearticle
Langueen
DomaineMedicine
ThématiqueTechnology and Human Factors in Education and Health
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSession (web analytics)Task (project management)CognitionComputer sciencePopulationPsychologyMental healthApplied psychologyMultimediaWorld Wide WebMedicinePsychotherapist

Résumé

récupéré en direct d'OpenAlex

Objectives – To explore the construction of mental models as a dynamic process and how users understand a consumer health information system, MedlinePlus, during a search session. 
 
 Design – Face-to-face interview. 
 
 Setting – Large university. 
 
 Subjects – A total of 38 undergraduate students participated in the study. All majoring in non-medical fields, such as art history, psychology, business, and communication studies. 
 
 Methods – Participants were randomized into two groups: the simple task group and the complex task group. Simple task group members were asked to perform 12 simple tasks while the complex group members performed three more-involved tasks. Simple tasks were defined as succinct questions with finite answers while complex tasks were open-ended and required more cognitive activity and synthesizing on the part of the individual. Participants in both groups were then given four simple tasks and two complex tasks to perform. Data was derived by video recording search sessions with individuals and interview-like questions for the tasks performed. Participants were given a brief introduction to the search session design and sessions took place in a private lab. Since the aim of the study was to track participants’ mental modeling processes over time, coding of data was caught at three different times throughout the search sessions: T1 (MM1) after five minutes of free exploration, T2 (MM2) after the first search session, and T3 (MM3) after the second search session. 
 
 Main Results – The author discusses the demographic specifics of the population participating in the study. Although participants were split into two groups, the results were combined to be more meaningful. Out of the 38 participants, 20 were female and 18 were male with ages ranging from 18 to 22. They had, on average, 10 years of computer experience and their average spatial ability score was 12.71. Also on average, they spent about 20 minutes completing the first search session and 12 minutes completing the second search session. The results show that participant-developed mental models of the MedlinePlus web space can be clustered into the following five theoretical components (this information is quantified in tables throughout the paper): system, content, information organization, interface, and procedural knowledge. 
 
 Conclusion – The study allowed participants to articulate their mental models and representations while conducting predefined searches during private sessions using MedlinePlus. The study also illustrates how users’ mental models of a system developed during interactions with an online system, on a theoretical level. Little is actually known about how mental models are developed when users interact with an information system. The study serves to explore this arena and reveals that the mental model construction involves changes and developments in three parallel dimensions: cognition, emotion, and behaviour. Also, these dimensions are accompanied by three mental activities: assimilating new concepts, phasing out previously perceived concepts, and modifying existing concepts. The mental model construction process could be a useful tool to build user models and make better design decisions for information systems.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCommunication savante
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,292
Score d'incertitude au seuil0,944

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,069
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,022
Tête enseignante GPT0,295
Écart entre enseignants0,273 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2013
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueEvidence Based Library and Information PracticeMême sujetTechnology and Human Factors in Education and HealthTravaux en français237 207