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Record W6981240702

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

2020· dissertation· en· W6981240702 on OpenAlexaboutno aff

Bibliographic record

VenueResearchSpace (University of Auckland) · 2020
Typedissertation
Languageen
FieldSocial Sciences
TopicMathematics Education and Teaching Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSet (abstract data type)Health carePerceptionMedical imagingAffect (linguistics)Patient educationMedical informationHealth education
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.006
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.362
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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