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Record W2937131082 · doi:10.1080/10401334.2019.1600520

Assigning Medical Students Learning Goals: Do They Do It, and What Happens When They Don't?

2019· article· en· W2937131082 on OpenAlex
Julian Manzone, Glenn Regehr, Shawn Garbedian, Ryan Brydges

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTeaching and Learning in Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsSt. Michael's HospitalNorth York General HospitalThe Wilson CentreUniversity Health NetworkUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsPsychologyCurriculumOutcome (game theory)Set (abstract data type)Variance (accounting)Analysis of varianceRandomized controlled trialMedical educationScale (ratio)Clinical psychologyApplied psychologySocial psychologyMedicineComputer sciencePedagogyMathematicsMachine learning

Abstract

fetched live from OpenAlex

Theory: Medical curricula now include more time for trainees to manage their studying independently, yet evidence suggests that time is not well spent without guidance. Social-cognitivist models of self-regulated learning suggest value when guiding learners to set goals related to their performance processes (actions producing outcomes) versus their performance outcomes (products of performance). Hypotheses: We expected participants oriented to set process goals would demonstrate better suturing skill retention compared with participants oriented to set outcome goals. Method: We randomly assigned 41 medical students to two groups: outcome oriented or process oriented. They self-scored their performance using a visual analog scale on every third trial during 25 training trials, and during 10 retention trials 2 weeks later. Two raters assessed participants’ suturing performances (process) and final products (outcome). After finding weak support for our hypothesis, we calculated a “self-monitoring calibration coefficient” as the Pearson’s correlation between the raters’ average score and each participant’s self-scores. We used a mixed-effects analysis of variance to compare participants’ performance scores as well as t tests and an analysis of variance to compare their self-monitoring calibration coefficients. Results: Analysis of skill retention data revealed a significant Group × Trial interaction, suggesting a benefit for the process group only for the 10th retention trial (p = .03). During training, the process group had significantly better (p = .02) self-monitoring calibration (r = .71 ± .29) than the outcome group (r = .38 ± .55). In retention, participants in both groups were significantly better calibrated (p = .04) with rater’s scores of performance processes (r = .39 ± .60) versus performance outcomes (r = .11 ± .63). Conclusions: Our findings provide limited evidence for our original hypothesis. Perhaps more important, however, our self-monitoring calibration data highlighted inconsistencies between our interventions and our participants’ apparent preferences. Not all participants adopted their assigned goal setting orientation, showing that researchers and educators must consider the extent to which trainees adopt imposed instructions in any educational intervention.

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.

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)0.015
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.355
Teacher spread0.339 · 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