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Record W4405330582 · doi:10.1002/jgc4.2007

Application of the RIME framework in genetic counseling fieldwork training to assess practice‐based competencies

2024· article· en· W4405330582 on OpenAlexaff
Deborah Cragun, Angela Trepanier, Nevena Krstić, Melissa Racobaldo, Paige Phillips Hunt, Susan Randall Armel

Bibliographic record

VenueJournal of Genetic Counseling · 2024
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsGenetic counselingPublic healthMedical educationTraining (meteorology)PsychologyHard rimeMedicineQuality of Life ResearchNursingApplied psychologyFamily medicineGeneticsGeography

Abstract

fetched live from OpenAlex

Using educational frameworks for learner assessment in genetic counseling (GC) training may help students and supervisors articulate developmentally appropriate clinical skills-based objectives and tasks that align with various stages of training as students work toward achieving entry-level competency. This professional issues case study describes how two GC programs adapted and implemented the RIME (Reporter-Interpreter-Manager-Educator) learner assessment framework, originally designed for medical education, to support and assess students' acquisition of practice-based competencies (PBCs) during clinical fieldwork placements. Each RIME level describes a different set of expectations regarding the skills students should be able to demonstrate based on the level of training they have achieved up to that point in time. In early training, students work mainly on gathering and reporting clinical information (Reporter level). In early to mid-training, students have learned what information to collect from clients and begin to apply the information to generate differential diagnoses (Interpreter level). When students reach the Manager level (typically by mid- to late-training), they can independently develop and implement case management plans tailored to individual cases. The Educator level, which may not be fully attained until after graduation, involves critically evaluating evidence and educating others about new evidence. The following paper describes our experiences incorporating the RIME framework into two GC graduate programs and explains the development of corresponding RIME-based assessment forms that align with the Accreditation Council of Genetic Counseling's 2023 PBCs. Overall, we find that using the RIME framework fosters a growth mindset by enabling students and supervisors to create developmentally appropriate goals and expectations, thereby facilitating assessment and guidance of trainee progress. Despite these perceived benefits, we acknowledge the need for research to evaluate the efficacy of the RIME framework or other learner assessment models in supporting student progression in achieving the GC PBCs.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.692
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 science0.0000.000
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 head0.035
GPT teacher head0.364
Teacher spread0.329 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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