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Record W3089182144 · doi:10.1080/13561820.2020.1807480

Transformative learning in an interprofessional student-run clinic: a qualitative study

2020· article· en· W3089182144 on OpenAlexaffabout
Enoch Ng, Tina Hu, Nancy McNaughton, Maria Athina Martimianakis

Bibliographic record

VenueJournal of Interprofessional Care · 2020
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsThe Wilson CentreUniversity Health NetworkMichener InstituteUniversity of Toronto
Fundersnot available
KeywordsTransformative learningThematic analysisContext (archaeology)Interprofessional educationPsychological interventionFocus groupPsychologyMedical educationQualitative researchHealth careSet (abstract data type)Free clinicMedicinePerspective (graphical)NursingPedagogySociology

Abstract

fetched live from OpenAlex

Student-run free clinics are increasingly seen as a way for students in health professions to have early authentic exposures to providing care to marginalized populations, often in the context of interprofessional teams. However, few studies characterize what and how students may learn from volunteering at a student-run free clinic. We aimed to examine shifts in attitude or practice that volunteers report after completing a placement at an interprofessional student-run clinic in Toronto, Ontario, Canada. Transcripts from semi-structured reflective focus groups were analyzed in an exploratory thematic manner and from the perspective of transformative learning theory. Volunteers reported attitude shifts toward greater self-awareness of assumptions, recognition of the need for systemic interventions, and seeing themselves as learning and contributing meaningfully in a team even without direct-client contact. Practice shifts emerged of individualizing assessment and treatment of patients as well as increased comfort working in interprofessional teams. Attitude and practice shifts were facilitated by authentic interactions with individuals from marginalized populations, taking a patient-centered approach, and an interprofessional context. Interprofessional student-run free clinics are suited to triggering disorienting dilemmas that set the stage for transformative learning, particularly when volunteers are guided to reflect.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.076
GPT teacher head0.562
Teacher spread0.487 · 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.

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".

Quick stats

Citations16
Published2020
Admission routes2
Has abstractyes

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