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Record W4381833078 · doi:10.15173/ijsap.v7i1.4937

Planning for co-curricular design-student voice, power dynamics and threshold learning: A thematic analysis of the student perspective

2023· article· en· W4381833078 on OpenAlexvenueno aff
Allison Anderson, Diana Austin, Christina Walton, Amanda Wood, Andrea Houlihan, Ella Hard, Kealey Bailey

Bibliographic record

VenueInternational Journal for Students as Partners · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisCurriculumReflexivityFocus groupPerspective (graphical)PsychologyPedagogyRelevance (law)Medical educationQualitative researchSociologyMedicineComputer sciencePolitical science

Abstract

fetched live from OpenAlex

A reflexive thematic analysis is applied to focus group data to understand how students experienced working in partnership with university staff and clinical professionals to co-design aspects of the curriculum. A qualitative descriptive approach is used to examine power dynamics, hierarchies, and student voice. Four themes are identified: students felt heard, students understood the relevance of and/or translation to professional practice, students described a shift in their perceived role in the project as well as shifts in hierarchical norms, and they reported feeling a sense of confidence. Insights are offered for applying the students-as-partners (SaP) framework to health education programmes. Relevant threshold learning concepts, cultural competency, and recently announced health system priorities in New Zealand are discussed and presented as both relevant and significant considerations. This analysis intends to offer a unique contribution towards health curriculum discussions, a recognised gap within the growing body of SaP literature.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.089
GPT teacher head0.578
Teacher spread0.489 · 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 designObservational
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

Citations10
Published2023
Admission routes1
Has abstractyes

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