MétaCan
Menu
Back to cohort
Record W3036271330 · doi:10.1177/2374373520933130

Teaching About Partnerships Between Patients and the Team: Exploring Student Perceptions

2020· article· en· W3036271330 on OpenAlexaff
Sylvia Langlois, Kamna Mehra

Bibliographic record

VenueJournal of Patient Experience · 2020
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsGeneral partnershipThematic analysisCurriculumInterprofessional educationMedical educationPsychologyPerceptionPerspective (graphical)MedicineHealth carePedagogyNursingQualitative researchSociologyPolitical science

Abstract

fetched live from OpenAlex

Health profession educators are responding to shifting approaches where patients are increasingly recognized as partners in an interprofessional care process. To foster competencies related to partnerships between patients and the team, educators have advanced the role of patient partners; however, an appreciation of resulting student learning is in its early stages. First-year students from 9 programs interacted with patient partners and participated in a Reader's Theater that explored partnerships with patients in an interprofessional team. Students completed reflective assignments; an inductive thematic analysis explored student learning. The following 4 overarching themes were recognized: developing insights through patient perspective, promoting partnerships with patients, recognizing attitudes that promote therapeutic relationships, and advocating for the patient to be a team member. Accompanying subthemes provide enhancement of each of the identified themes. Students discussed the effect of poor collaboration, identified attitudes that promote collaboration, and expressed the value of advocacy for patient partnership. An enriched appreciation of student learning will guide educator engagement of patient partners in both health professional and interprofessional curricula.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.143
GPT teacher head0.459
Teacher spread0.316 · 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 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

Citations12
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Patient ExperienceSame topicInterprofessional Education and CollaborationFrench-language works237,207