MétaCan
Menu
Back to cohort

Directed self‐regulated learning versus instructor‐regulated learning in simulation training

2012· article· en· W1592101778 on OpenAlex

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

VenueMedical Education · 2012
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of CalgaryThe Wilson CentreUniversity of TorontoUniversity of British ColumbiaUniversity Health Network
Fundersnot available
KeywordsChecklistAnalysis of varianceTest (biology)Repeated measures designPsychologyCorrelationConfidence intervalPearson product-moment correlation coefficientMedicinePhysical therapyStatisticsMathematicsInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Simulation training offers opportunities for unsupervised, self-regulated learning, yet little evidence is available to indicate the efficacy of this approach in the learning of procedural skills. We evaluated the effectiveness of directed self-regulated learning (DSRL) and instructor-regulated learning (IRL), respectively, for teaching lumbar puncture (LP) using simulation. METHODS: We randomly assigned internal medicine residents in postgraduate year 1 to either DSRL ('directed' to progress from easy to difficult LP simulators during self-regulated learning) or IRL (in groups of four led by an instructor). All participants practised for up to 50 minutes and completed a pre-test, post-test and delayed (by 3 months) retention test on the simulator. Pairs of blinded trained experts independently rated all videotaped performances using a validated global rating scale and a modified version of a validated checklist. Participants provided measures of LP experience and self-reported confidence. We analysed the pre-post (n = 42) and pre-post-retention performance scores (n = 23) using two separate repeated-measures analyses of variance (anovas) and computed Pearson correlation coefficients between participants' confidence and performance scores. RESULTS: Inter-rater agreement was strong for both performance measures (intra-class correlation coefficient > 0.81). The groups achieved similar pre-test and post-test scores (p > 0.05) and scores in both groups improved significantly from the pre- to the post-test (p < 0.05). On retention, a significant interaction (F(2,42) = 3.92, p = 0.03) suggests the DSRL group maintained its post-test performance, whereas that in the IRL group dropped significantly (p < 0.05). Correlations between self-reported confidence and post-test performance were positive and significant for the DSRL group, and negative and non-significant for the IRL group. CONCLUSIONS: Both IRL and DSRL led to improved LP performance immediately after practice. Whereas the IRL group's skills declined after 3 months, the DSRL group's performance was maintained, suggesting a potential long-term benefit of this training. Participants in the DSRL group also developed a more accurate relationship between confidence and competence following practice. Further research is needed to clarify the mechanisms of self-regulated learning and its role in simulation contexts.

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)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), 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 score0.180
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.381
Teacher spread0.344 · 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