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Record W2100881081 · doi:10.1177/0163278713518942

Notes From the Field

2014· article· en· W2100881081 on OpenAlexaff
Irene Ma, Nadia Zalunardo, Mary Brindle, Rose Hatala, Kevin McLaughlin

Bibliographic record

VenueEvaluation & the Health Professions · 2014
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsUniversity of British ColumbiaUniversity of Calgary
Fundersnot available
KeywordsModalitiesChecklistModality (human–computer interaction)Video recordingReliability (semiconductor)Video qualityMedicineMedical physicsComputer sciencePsychologyMultimediaArtificial intelligenceCognitive psychologyOperations managementEngineering

Abstract

fetched live from OpenAlex

Blinded assessments of technical skills using video-recordings may offer more objective assessments than direct observations. This study seeks to compare these two modalities. Two trained assessors independently assessed 18 central venous catheterization performances by direct observation and video-recorded assessments using two tools. Although sound quality was deemed adequate in all videos, portions of the video for wire handling and drape handling were frequently out of view (n = 13, 72% for wire-handling; n = 17, 94% for drape-handling). There were no differences in summary global rating scores, checklist scores, or pass/fail decisions for either modality (p > 0.05). Inter-rater reliability was acceptable for both modalities. Of the 26 discrepancies identified between direct observation and video-recorded assessments, three discrepancies (12%) were due to inattention during video review, while one (4%) discrepancy was due to inattention during direct observation. In conclusion, although scores did not differ between the two assessment modalities, techniques of video-recording may significantly impact individual items of assessments.

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)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.766
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.151
GPT teacher head0.487
Teacher spread0.336 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2014
Admission routes1
Has abstractyes

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