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Record W4281941118 · doi:10.4995/head22.2022.14286

Designing a Novel Interprofessional and Inter-University Education Session for Healthcare Trainees to Improve Interprofessional Practice

2022· article· en· W4281941118 on OpenAlexaff
Justine Hamilton, Ashwini Namasivayam‐MacDonald, Linnéa Shackel, Heather MacPhee

Bibliographic record

Venue8th International Conference on Higher Education Advances (HEAd'22) · 2022
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsUniversity of ManitobaMcMaster University
Fundersnot available
KeywordsInterprofessional educationComputer scienceSession (web analytics)Health careAsynchronous communicationMedical educationPharmacyMedicineNursing

Abstract

fetched live from OpenAlex

Interprofessional education is widely acknowledged as critical for training successful clinicians, however logistical challenges often interfere with its implementation. The aim of this paper is to describe the procedures developed to enable students in different health professional programs in different geographic regions within the same country to learn about each other’s professions and apply this knowledge to optimize outcomes for patients. Principles from the Rehabilitation Treatment Specification System and Universal Design for Learning were combined to design an efficient and effective virtual approach to achieving interprofessional knowledge and collaborative skill outcomes. Application of these principles resulted in a 3-stage approach combining synchronous and asynchronous learning as well as didactic and problem-based learning. This paper describes the design and implementation for speech-language pathology and pharmacy students learning about swallowing disorders, but the procedures are applicable to a broad range of professions and academic content when interprofessional education is the goal.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.843
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.081
GPT teacher head0.486
Teacher spread0.405 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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