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Record W2803418995 · doi:10.1093/pch/pxz169

Preparing for CBME: How often are faculty observing residents?

2020· article· en· W2803418995 on OpenAlexaffabout
Sheenagh George, Sarah Manos, Kenny K. Wong

Bibliographic record

VenuePaediatrics & Child Health · 2020
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDemographicsMedicineCompetence (human resources)Family medicineProgram directorGraduate medical educationDescriptive statisticsMedical educationPsychologyAccreditation

Abstract

fetched live from OpenAlex

BACKGROUND: The Royal College of Physicians and Surgeons of Canada officially launched 'Competence by Design' in July 2017, moving from time-based to outcomes-based training. Transitioning to competency-based medical education (CBME) necessitates change in resident assessment. A greater frequency of resident observation will likely be required to adequately assess whether entrustable professional activities have been achieved. PURPOSE: Characterize faculty and resident experiences of direct observation in a single paediatric residency program, pre-CBME implementation. Qualitatively describe participants' perceived barriers and incentives to participating in direct observation. METHODS: Surveys were sent to paediatric residents and faculty asking for demographics, the frequency of resident observation during an average 4-week rotation, perceived ideal frequency of observation, and factors influencing observation frequency. Descriptive data were analyzed. Institutional research ethics board approval was received. RESULTS: The response rate was 54% (34/68 faculty and 16/25 residents). When asked the MAXIMUM frequency FACULTY observed a resident take a history, perform a physical examination, or deliver a plan, the median faculty reply was 1, 2, and 3, for outpatient settings and 0, 1, and 2, for inpatient settings. The median RESIDENT reply was 2, 4, and 10 for outpatient settings and 1, 2, and 20 for inpatient settings. When asked the MINIMUM frequency for each domain, the median FACULTY and RESIDENT reply was 0, except for delivering a plan in the inpatient setting. Faculty reported observing seniors delivering the plan more frequently than junior residents. Faculty and resident median replies for how frequently residents should be observed for each domain were the same, three to four, three to four, and five to six times. Four per cent of faculty reported regularly scheduling observations, and 77% of residents regularly ask to be observed. The most common barriers to observation were too many patients to see and both faculty and residents were seeing patients at the same time. Most faculty and resident responders felt that observation frequency could be improved if scheduled at the start of the rotation; faculty were provided a better tool for assessment; and if residents asked to be observed. CONCLUSIONS: This study provides baseline data on how infrequent faculty observation is occurring and at a frequency lower than what faculty and residents feel is necessary. The time needed for observation competes with clinical service demands, but better scheduling strategies and assessment tools may help.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.078
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.078
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.054
GPT teacher head0.356
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainEvaluation
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
Published2020
Admission routes2
Has abstractyes

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