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Record W2890596533 · doi:10.1186/s12909-018-1327-7

Avoid reinventing the wheel: implementation of the Ottawa Clinic Assessment Tool (OCAT) in Internal Medicine

2018· article· en· W2890596533 on OpenAlexafffundabout
Samantha Halman, Janelle Rekman, Timothy J. Wood, Andrew Baird, Wade Gofton, Nancy Dudek

Bibliographic record

VenueBMC Medical Education · 2018
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsContext (archaeology)Variance (accounting)Descriptive statisticsMedical educationPsychometricsReliability (semiconductor)Educational measurementMedicinePsychologyApplied psychologyCurriculumClinical psychologyStatistics

Abstract

fetched live from OpenAlex

BACKGROUND: Workplace based assessment (WBA) is crucial to competency-based education. The majority of healthcare is delivered in the ambulatory setting making the ability to run an entire clinic a crucial core competency for Internal Medicine (IM) trainees. Current WBA tools used in IM do not allow a thorough assessment of this skill. Further, most tools are not aligned with the way clinical assessors conceptualize performances. To address this, many tools aligned with entrustment decisions have recently been published. The Ottawa Clinic Assessment Tool (OCAT) is an entrustment-aligned tool that allows for such an assessment but was developed in the surgical setting and it is not known if it can perform well in an entirely different context. The aim of this study was to implement the OCAT in an IM program and collect psychometric data in this different setting. Using one tool across multiple contexts may reduce the need for tool development and ensure that tools used have proper psychometric data to support them. METHODS: Psychometrics characteristics were determined. Descriptive statistics and effect sizes were calculated. Scores were compared between levels of training (juniors (PGY1), seniors (PGY2s and PGY3s) & fellows (PGY4s and PGY5s)) using a one-way ANOVA. Safety for independent practice was analyzed with a dichotomous score. Variance components were generated and used to estimate the reliability of the OCAT. RESULTS: Three hundred ninety OCATs were completed over 52 weeks by 86 physicians assessing 44 residents. The range of ratings varied from 2 (I had to talk them through) to 5 (I did not need to be there) for most items. Mean scores differed significantly by training level (p < .001) with juniors having lower ratings (M = 3.80 (out of 5), SD = 0.49) than seniors (M = 4.22, SD = - 0.47) who had lower ratings than fellows (4.70, SD = 0.36). Trainees deemed safe to run the clinic independently had significantly higher mean scores than those deemed not safe (p < .001). The generalizability coefficient that corresponds to internal consistency is 0.92. CONCLUSIONS: This study's psychometric data demonstrates that we can reliably use the OCAT in IM. We support assessing existing tools within different contexts rather than continuous developing discipline-specific instruments.

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)0.005
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.030
GPT teacher head0.457
Teacher spread0.427 · 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 designObservational
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

Citations25
Published2018
Admission routes3
Has abstractyes

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