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The Effects of an Anatomy Near‐Peer Learning Activity on Interprofessional Stereotypes: A Mixed Methods Study

2018· article· en· W3175683116 on OpenAlexaffabout
Nickoo Merati, Anna Murphy‐Buske, Patricia Alfaro, Sandie Larouche, Geoffroy Noël, Nicole M. Ventura

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsChamplain Regional CollegeMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsTUTORInterprofessional educationMedical educationHealth carePerceptionPsychologyHealth professionalsFocus groupMedicineNursingPedagogy

Abstract

fetched live from OpenAlex

Beyond their clinical rotations, healthcare students have few opportunities to interact with students in other domains of the health field. Furthermore, stereotypes exist within and between healthcare disciplines that can negatively impact interprofessional communication and collaboration in the clinical setting. While the anatomical sciences remain a common field of study for both nursing and medical disciplines, strategies that utilize the anatomy laboratory as a setting to minimize negative interprofessional attitudes have yet to be well characterized. Driven by a need to better understand healthcare trainees' interprofessional stereotypes, and the potential of the anatomy lab as a common teaching and learning environment, this study aims to assess the effects of an anatomy interprofessional near‐peer learning activity (AIP‐NPLA) on medical and nursing students' interprofessional stereotypes at McGill University. We employed a convergent parallel mixed methods study design to obtain a complete understanding of participating students' AIP‐NPLA experiences, where each healthcare trainee group had the opportunity to take on the role of both tutor and tutee. Using the Attitudes to Health Professionals Questionnaire (AHPQ), we quantitatively assessed participating students' perceived attitudes and stereotypes on both their own and their counterpart's profession, before and after the AIP‐NPLA. Using semi‐structured focus groups, we qualitatively explored participating students' experiences and perceptions of their interprofessional stereotypes after the AIP‐NPLA. Quantitative results demonstrated minimal changes in the pre‐ and post‐AIP‐NPLA responses from the medical students, which may be attributable to lower medical student completion. However, data from the nursing students demonstrated significant changes in ten of the twenty AHPQ attributes with respect to their perceived stereotypes of the medical profession. These changes included an increased regard for the medical profession's empathy, approachability and valuing team‐work. Furthermore, nursing student responses changed significantly for three of the twenty attributes when evaluating their own profession. Qualitative results also spoke to suggest a breakdown of negative stereotypes, an increased understanding of inter‐ and intra‐professional roles in a team, and the importance of interprofessional collaboration and mutual learning for their future careers. Taken together, these results demonstrate that by establishing a relevant setting for shared education, stereotypes among healthcare trainees may be positively restructured, allowing for effective and collaborative care to predominate in the current and future clinical setting. Support or Funding Information Jonathan Campbell Meakins and Family Memorial Fellowship This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.016
metaresearch head score (Gemma)0.020
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 score0.016
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.029
GPT teacher head0.495
Teacher spread0.466 · 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".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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