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Record W2110147379 · doi:10.1155/2014/219512

Correlation between Global Rating Scale and Specific Checklist Scores for Professional Behaviour of Physical Therapy Students in Practical Examinations

2014· article· en· W2110147379 on OpenAlexafffund
Kaitlin Turner, Maegan Bell, Lindsay Bays, Carmen Lau, Clara Lai, Tetyana Kendzerska, Cathy Evans, Robyn Davies

Bibliographic record

VenueEducation Research International · 2014
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
FundersUniversity of Toronto
KeywordsChecklistRating scaleCorrelationSignificant differenceScale (ratio)PsychologyClinical psychologyMedicineDevelopmental psychologyInternal medicineMathematicsCartography

Abstract

fetched live from OpenAlex

The purpose of this study was to determine whether or not the specific item checklist (checklist) and global rating scale (GRS) scores are correlated in practical skills examinations (PSEs). Professional behaviour was evaluated using both the checklist and GRS scores for 183 students in three PSEs. Mean, standard deviation, and correlation for checklist and GRS scores were calculated for each station, within each PSE. Pass rate for checklist and GRS was determined for each PSE, as well as for each individual checklist item within each PSE. Overall, pass rate was high for both checklist and GRS evaluations of professional behaviour in all PSEs. Generally, mean scores for the checklist and GRS were high, with low standard deviations, resulting in low data variability. Spearman correlation between total checklist and GRS scores was statistically significant for two out of five stations in PSE 1, five out of six stations in PSE 2, and three out of four stations in PSE 3. The GRS is comparable to the checklist for evaluation of professional behaviour in physical therapy (PT) students. The correlation between the checklist and GRS appears to become stronger in the assessment of more advanced students.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.398

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 integrity0.0000.000
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 head0.084
GPT teacher head0.537
Teacher spread0.452 · 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 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

Citations10
Published2014
Admission routes2
Has abstractyes

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