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Record W2038571943 · doi:10.1097/acm.0b013e3181b37b4d

Scaffolding Knowledge Building in a Web-Based Communication and Cultural Competence Program for International Medical Graduates

2009· article· en· W2038571943 on OpenAlexafffundabout
Leila Lax, Mackenzie Russell, Laura Jayne Nelles, Cathy M. Smith

Bibliographic record

VenueAcademic Medicine · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsUniversity of Toronto
FundersHealthForceOntario
KeywordsCultural competenceCompetence (human resources)Medical educationScaffoldPsychologyKnowledge managementComputer scienceMedicinePedagogyBiomedical engineeringSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Professional behaviors, tacitly understood by Canadian-trained physicians, are difficult to teach and often create practice barriers for IMGs. The purpose of this design research study was to develop a Web-based program simulating Canadian medical literacy and culture, and to evaluate strategies of scaffolding individual knowledge building. METHOD: Study 1 (N = 20) examined usability and pedagogic design. Studies 2 (N = 39) and 3 (N = 33) examined case participation patterns. RESULTS: Model design was validated in Study 1. Studies 2 and 3 demonstrated high levels of participation, on unprompted third tries, on knowledge tests. Recursive patterns were strongest on Reflective Exercises. Five strategies scaffolded knowledge building: (1) video simulations, (2) contextualized resources, (3) concurrent feedback, (4) Reflective Exercises, and (5) commentaries prompting "reflection on reflection." CONCLUSIONS: Scaffolded design supports complex knowledge building. These findings are concurrent with educational research on the importance of recursion and revision of knowledge for improvable and relational understanding.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.069
GPT teacher head0.456
Teacher spread0.387 · 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 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

Citations19
Published2009
Admission routes3
Has abstractyes

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