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Does Every Picture Tell a Story? The Use of Medical Images for Patient Education

2020· dissertation· en· W6981240702 sur OpenAlexaboutno aff

Notice bibliographique

RevueResearchSpace (University of Auckland) · 2020
Typedissertation
Langueen
DomaineSocial Sciences
ThématiqueMathematics Education and Teaching Techniques
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSet (abstract data type)Health carePerceptionMedical imagingAffect (linguistics)Patient educationMedical informationHealth education
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

A substantial body of research has shown that visual aids can enhance verbal medical information. However, from the literature, it is unclear what type of visual aid is the most effective. Medical images can be a promising vehicle for health communication: several studies have reported that feedback of medical images improved patients’ understanding of health information, illness beliefs and compliance with medical advice. On the other hand, the effects of medical images are not fully understood, as this type of research is still in its early stages. This thesis aimed to explore how medical images can be effectively used for patient education. More specifically, in a set of studies, this thesis evaluated the impact of medical images on attention, the understanding of health information, illness and treatment beliefs and how the addition of images impacts on the perception of the educational material. The images were tested using different methods of presentation, such as printed patient education material (PEM), computer-based information and during face-to-face interventions. The effects of medical images were compared to the effects of unillustrated information and other types of images such as cartoons, anatomical drawings and photographs. The broad goal of this work was to gain new insights into how medical images affect patients and how such images could be incorporated into healthcare practice. This thesis comprises four studies; the first was a content analysis of images used in existing PEM about gout. The study identified 310 images in 71 publicly available online educational resources about gout. The resources were from medical and health organisations and health education websites from Australia, Canada, Ireland, New Zealand, South Africa, UK and USA. The content analysis found that key concepts about gout and treatment were underrepresented, and a large proportion of images did not convey any information about gout. Moreover, about a third of gout PEM did not include any images. The second study evaluated how the addition of a medical illustration to an educational leaflet and the type of the illustration affected people’s understanding, illness beliefs and the perception of the material. Two hundred and four members of the general public were recruited in a local supermarket. The participants saw one of the four leaflets about gout: a text-only leaflet or a leaflet illustrated with either a cartoon, an anatomical drawing or a medical scan. The study that pictures aided the understanding of information, increased the visual appeal of material but had no effects on illness perceptions about gout. Out of the three image types, cartoons were the most helpful for improving the understanding, but people preferred a more detailed anatomical image; the medical scan offered no benefits. The third study evaluated the educational effects of computer-based material about gout based on either a text without images, text with medical images or text with images taken from existing PEM about gout. One hundred and fifty-eight university students, staff and members of the general public were recruited through university advertisements. The study found no negative effects of medical images on people’s understanding of gout. Moreover, medical images made the material more visually appealing, and compared to images from existing PEM, evoked more interest and feelings of control. The final study explored how the personalisation of medical images influenced illness perceptions, medication beliefs and treatment understanding in people with gout. Sixty patients with a confirmed diagnosis of gout took part in the study. Either personal medical images, generic medical images or images from an existing gout PEM were embedded into a face-to-face educational presentation about gout. The study found that all three interventions favourably influenced illness understanding, medication beliefs and illness perceptions. Personalisation of images made the information more interesting and helpful. Overall, the findings from this work suggest that medical images have no adverse effects on people and can be incorporated into PEM. Moreover, when explained appropriately, these images can induce more interest and increase the visual appeal of the material. Medical images yield more benefits when they are personalised and shown to patients during a longer face-toface consultation rather than embedded in shorter printed leaflets. This thesis contributes to the literature by providing further evidence of the superiority of illustrated PEM over unillustrated. Furthermore, it addresses the gaps in the understanding of how medical images compare to other types of pictures in their effects on people, and in what form medical images should be presented to patients. The work reported in this thesis can inform the development of materials for patients. Future research needs to explore what types of medical images are the most suitable for patient education, and if interventions based on medical images can induce positive long-term changes in patients’ health outcomes and well-being.

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)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,337
Score d'incertitude au seuil0,616

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
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 Opus0,052
Tête enseignante GPT0,362
Écart entre enseignants0,310 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
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
Publié2020
Routes d'admission1
Résumé présentoui

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