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Record W4392138013 · doi:10.1111/medu.15369

Exploring equity, diversity and inclusivity through the Art of Observation

2024· article· en· W4392138013 on OpenAlexaboutno aff
Sigi Maho, Natalie McGuire, Rachel Curtis, Christine Law

Bibliographic record

VenueMedical Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsEquity (law)Diversity (politics)Health equityPsychologySociologyPolitical scienceMedicineNursingAnthropologyPublic healthLaw

Abstract

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Medicine always takes place within a cultural and ethical context which necessitates trainees to develop an understanding of the health humanities. Art education has been included in medical school curricula to introduce medical students to new ways of fostering communication, empathy, curiosity, flexible thinking, observational skills and awareness of personal bias. Yet a survey conducted on first-year medical students at Queen's University identified that while most respondents had a working knowledge of equity, diversity, inclusion (EDI) principles, many felt inadequately trained and would benefit from a more interdisciplinary and interdepartmental approach in this realm. The purpose of this study was to address this gap within medical education and augment the existing curricula on EDI through an interdisciplinary art-based intervention. The Art of Observation programme at the Agnes Etherington Centre at Queen's University was delivered to a small group of first-year students in the School of Medicine as a multi-session programme, taking place over two, 2-hour sessions, spread over two weeks. During the first session, students engaged in transmediation by participating in a body scan meditation and later translating the sensations experienced during the meditation into sculptures using clay medium. Through this mindful art making process, students were offered a modality to consider their own embodiment and further explore self-expression, inclusivity and diversity. The second session focused on art observation and visual analysis skills. Using culturally diverse artwork from various regions of the world, such as Canadian Indigenous communities, East India and the African continent, students worked through inquiry-based learning approaches to perform visual analyses of the selected art works. The overall aims of both sessions were for students to become more aware of personal assumptions and biases within the context of EDI, enhance their ability to perceive details and interpret emotional language, and improve their verbal communication and description skills. The research team conducted pre- and post-tests for each session to measure the impact of these sessions on student's attitudes towards art and its ability to be utilised as an augmentative tool to address principles of EDI within the medical curriculum. The pre- and post-tests for each session were analysed using qualitative methods of Likert scales and thematic analysis. Overall, post-test results from both sessions demonstrated an improvement in attitudes towards using art not only to foster self-expression but also to further express and explore issues of EDI. This improvement was found to be stronger with the meditation and sculpting session compared with the art observation session. Respondents also strongly agreed that each session was interactive, understandable, useful and applicable. These views were again stronger after the meditation and sculpture session. Most importantly, this study provides some evidence that self-expression, hands-on, non-traditional medical teaching may promote the adoption of EDI principles among medical students more than standard art observations. This reinforces the ongoing need to integrate art-based learning across curricula and branch out of traditional teaching pedagogy in medicine in order to achieve equity objectives that inherently require transformational approaches to learning. Christine Law conceptualised and designed the project and wrote and edited the manuscript. Sigi Maho performed data collation and analysis and wrote and edited the manuscript. Rachel Curtis conceptualised and designed the project, facilitated the first session and organized survey distribution and collection. Natalie McGuire recruited participants, facilitated study communication and developed study materials including surveys. The data that support the findings of this study are available in the supporting information of this article.

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)0.007
metaresearch head score (Gemma)0.007
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.008
Scholarly communication0.0030.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.650
GPT teacher head0.604
Teacher spread0.046 · 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
Published2024
Admission routes1
Has abstractyes

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