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Record W2742459654 · doi:10.36834/cmej.36948

Time is of the essence: an observational time-motion study of internal medicine residents while they are on duty

2017· article· en· W2742459654 on OpenAlexaffvenue
Cameron Leafloor, Erin Y. Liu, Cathy Code, Heather Lochnan, Deanna M. Rothwell, Alan J. Forster, Allen Huang

Bibliographic record

VenueCanadian Medical Education Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsOttawa HospitalMcGill UniversityUniversity of Ottawa
Fundersnot available
KeywordsDutyDocumentationMedicineObservational studyWorkflowTime allocationTime managementMedical emergencyFamily medicineComputer scienceInternal medicineDatabase

Abstract

fetched live from OpenAlex

BACKGROUND: The effects of changes to resident physician duty hours need to be measureable. This time-motion study was done to record internal medicine residents' workflow while on duty and to determine the feasibility of capturing detailed data using a mobile electronic tool. METHODS: . RESULTS: A total of 17,714 events were recorded, encompassing 516 hours of observation. Time was apportioned in the following categories: Direct Patient Care (22%), Communication (19%), Personal tasks (15%), Documentation (14%), Education (13%), Indirect care (11%), Transit (6%), Administration (0.6%), and Non-physician tasks (0.4%). Nineteen percent of the education time was spent in self-directed learning activities. Only 9% of the total on duty time was spent in the presence of patients. Sixty-five percent of communication time was devoted to information transfer. A total of 968 interruptions were recorded which took on average 93.5 seconds each to service. CONCLUSION: Detailed recording of residents' workflow is feasible and can now lead to the measurement of the effects of future changes to residency training. Education activities accounted for 13% of on-duty time.

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)0.001
metaresearch head score (Gemma)0.011
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 score0.175
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.000

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 head0.043
GPT teacher head0.361
Teacher spread0.318 · 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

Citations6
Published2017
Admission routes2
Has abstractyes

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