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Record W2790697941 · doi:10.1111/medu.13501

‘Can the patient speak?’: postcolonialism and patient involvement in undergraduate and postgraduate medical education

2018· article· en· W2790697941 on OpenAlexaff
Malika Sharma

Bibliographic record

VenueMedical Education · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsThe Wilson CentreMaple Leaf Medical ClinicUniversity of Toronto
Fundersnot available
KeywordsPostcolonialism (international relations)Medical educationMedicinePsychologySociologyGender studies

Abstract

fetched live from OpenAlex

CONTEXT: Patients are increasingly being engaged in providing feedback and consultation to health care institutions, and in the training of health care professionals. Such involvement has the potential to disrupt traditional doctor-patient power dynamics in significant ways that have not been theorised in the medical literature. Critical theories can help us understand how power flows when patients are engaged in the training of medical students. METHODS: This paper applies postcolonial theory to the involvement of patients in the development and delivery of medical education. First, I review and summarise the literature around patient involvement in medical education. Subsequently, I highlight how postcolonial frameworks have been applied to medical education more broadly, extrapolating from the literature to apply a postcolonial lens to the area of patient engagement in medical education. CONCLUSION: Concepts from postcolonial theory can help medical educators think differently about how patients can be engaged in the medical education project in ways that are meaningful and non-tokenistic. Specifically, the positioning of the patient as 'subaltern' can provide channels of resistance against traditional power asymmetries. This has curricular and methodological implications for medical education research in the area of patient engagement.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.404
Teacher spread0.354 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

Quick stats

Citations56
Published2018
Admission routes1
Has abstractyes

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