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Record W3031096959 · doi:10.1097/acm.0000000000003528

Does Educational Handover Influence Subsequent Assessment?

2020· article· en· W3031096959 on OpenAlexaff
Valérie Dory, Deborah Danoff, Laurie H. Plotnick, Beth‐Ann Cummings, Carlos Gomez‐Garibello, Nicole E. Pal, Stephanie T. Gumuchian, Meredith Young

Bibliographic record

VenueAcademic Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsMontreal Children's HospitalMcGill UniversitySocial Sciences and Humanities Research CouncilMcGill University Health Centre
Fundersnot available
KeywordsStrengths and weaknessesHandoverPsychologyRandomized controlled trialControl (management)SupervisorMedical educationNarrativeApplied psychologySocial psychologyMedicineComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: Educational handover (i.e., providing information about learners' past performance) is controversial. Proponents argue handover could help tailor learning opportunities. Opponents fear it could bias subsequent assessments and lead to self-fulfilling prophecies. This study examined whether raters provided with reports describing learners' minor weaknesses would generate different assessment scores or narrative comments than those who did not receive such reports. METHOD: In this 2018 mixed-methods, randomized, controlled, experimental study, clinical supervisors from 5 postgraduate (residency) programs were randomized into 3 groups receiving no educational handover (control), educational handover describing weaknesses in medical expertise, and educational handover describing weaknesses in communication. All participants watched the same videos of 2 simulated resident-patient encounters and assessed performance using a shortened mini-clinical evaluation exercise form. The authors compared mean scores, percentages of negative comments, comments focusing on medical expertise, and comments focusing on communication across experimental groups using analyses of variance. They examined potential moderating effects of supervisor experience, gender, and mindsets (fixed vs growth). RESULTS: Seventy-two supervisors participated. There was no effect of handover report on assessment scores (F(2, 69) = 0.31, P = .74) or percentage of negative comments (F(2, 60) = 0.33, P = .72). Participants who received a report indicating weaknesses in communication generated a higher percentage of comments on communication than the control group (63% vs 50%, P = .03). Participants who received a report indicating weaknesses in medical expertise generated a similar percentage of comments on expertise compared to the controls (46% vs 47%, P = .98). CONCLUSIONS: This study provides initial empirical data about the effects of educational handover and suggests it can-in some circumstances-lead to more targeted feedback without influencing scores. Further studies are required to examine the influence of reports for a variety of performance levels, areas of weakness, and learners.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.291
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.363
Teacher spread0.335 · 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
Published2020
Admission routes1
Has abstractyes

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