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Record W4388048465 · doi:10.54531/hvpn9537

A21 The use of simulated learning in prequalifying physiotherapy education: a scoping review

2023· review· en· W4388048465 on OpenAlexaboutno aff
Jonathan Room, Robyn Stiger

Bibliographic record

VenueInternational Journal of Healthcare Simulation · 2023
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical therapyPhysical medicine and rehabilitationPsychologyMedicine

Abstract

fetched live from OpenAlex

Background and aim: Simulation-based learning is an increasingly popular pedagogical approach. In some areas of physiotherapy, it is better been documented, for example, cardiorespiratory physiotherapy [1]. However, its use in other physiotherapy-related settings is less clear. Therefore, the aim of this project was to review the literature on simulation-based learning in prequalifying physiotherapy education, in order to explore where studies have taken place, which physiotherapy settings it is used in and indication of its effectiveness in teaching. Methods: This study was carried out based on the scoping review methodology outlined by Arksey and O’Malley [2]. The following databases were searched: AMED, BNI, CINAHL, Embase, Emcare, HMIC, Medline and PsychInfo, using specific search terms, to find studies involving the use of simulation in a prequalifying physiotherapy setting. Returned papers were screened using inclusion and exclusion criteria by two reviewers. The database search results were recorded and managed using Rayyan™ [3]. Results: The database search retrieved 280 papers. Following the removal of duplicates, screening titles and abstracts and then screening full-text papers, 39 papers were included. The included studies were conducted in USA (n = 23), Australia (n = 10), Canada (n = 1), Finland (n = 1), Germany (1), Spain (1), Taiwan (1), UK (1). Simulation-based learning activities took place in a variety of physiotherapy settings. Most took place in an acute care or cardiorespiratory setting. There was a high level of variation in the reporting of the described simulation activity. This made it difficult to establish whether simulations were of high or low fidelity. Where reporting was well described, simulation activities tended to follow a framework of pre-brief, simulation and then debriefing. The majority of studies reported some measure of the effectiveness or feasibility of simulation-based learning. Conclusion: This scoping review identified a growing body of evidence supporting simulation-based learning in prequalifying physiotherapy education. However, to date, its use in pedagogical research has tended to focus on the cardiorespiratory setting, and it has often been researched as a tool to explore or enhance interprofessional collaboration. Whilst both of these areas are of value to the profession, there is scope to explore the use of simulation-based learning in settings such as musculoskeletal teaching. Further work on its use and value in the teaching of discrete complex tasks, in addition to collaborative practice, such as team working, de-escalation and communication is also needed. Ethics statement: Authors confirm that all relevant ethical standards for research conduct and dissemination have been met. The submitting author confirms that relevant ethical approval was granted, if applicable.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.860
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.357
GPT teacher head0.657
Teacher spread0.301 · 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 designOther design
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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