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Record W1648989863

Living labs: The case of point-of-care ultrasound education

2015· article· en· W1648989863 on OpenAlexaff
Adam Dubrowski, Brian L. Metcalfe, Mike Parsons, Tia Renouf, Peter Rogers, Gillian Sheppard, Andrew Smith, Holly A. Black, Heather McCarthy, Jordon Stone-Mclean

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPoint of care ultrasoundMedicineUltrasoundComputer scienceRadiology
DOInot available

Abstract

fetched live from OpenAlex

Background/rationale: This workshop highlights the use of a research support process and framework to transform an educational program into a living lab.The framework emphasizes the importance of using theory, the evaluation of acceptability and feasibility of educational innovation, rigorous testing, and forecasting the integration within the educational system.The research support processes include engagement of a group of educators in research skills development workshops, formation of research streams, purposeful assignments of trainees and supervisors, and implementation of communication strategies to ensure proper orchestration of the efforts.Together, this leads to a development of a living lab, which refers to an environment that integrates research and educational innovation processes though the co-creation, exploration, experimentation, and evaluation of innovative ideas related to teaching.Objectives: At the end of the workshop, the participants will be (1) familiar with the concept of living labs;(2) able to apply the concept to their own contexts; (3) understand the difference between project and program-based research; (4) follow the research framework; and (5) identify key steps necessary to develop a program of research.Teaching Methods: Initially, we will describe the research framework and the research processes implemented by our group in the formation of a living lab.Next, using staged interviews we will highlight how these two components enhance the research experience through multiple lenses such as student, researcher, and clinician.Finally, there will be two hands-on activities.Activity 1: Generating programmatic research questions.Activity 2: Turning projects into programs.Five minutes introduction -20 minutes lecture -20 minutes staged interview with the team -5 minutes independent work (Activity 1) -5 minutes debrief on Activity 1 -10 minutes group work (Activity 2) -20 minutes presentations and debrief on Activity 2 -5 minutes concluding remarks and evaluation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.223
Teacher spread0.211 · 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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