MétaCan
Menu
Back to cohort
Record W2330407772 · doi:10.2202/1548-923x.2197

Implementing Team Based Learning in Large Classes: Nurse Educators' Experiences

2011· review· en· W2330407772 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

VenueInternational Journal of Nursing Education Scholarship · 2011
Typereview
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsWorkloadPreparednessTeam-based learningClass (philosophy)AttritionStatus quoNursingMedical educationReading (process)DebriefingPsychologyMedicineComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Team-based learning (TBL) is an interactive teaching method promoted as an alternative to traditional lectures. TBL was implemented in four large second year classes in a baccalaureate nursing program but the implementation process was found to require much more effort than indicated in the literature. A predominant theme during the implementation phase was the importance of collegial support. Faculty workload increased significantly and they were challenged by occasional student confrontations and technological difficulties. The benefits for students included reduced attrition, reading workloads, and enhanced preparedness for classes, which allowed for more time to be spent in class discussing complex realistic nursing problems. Although TBL was not enthusiastically embraced by all of the students, the majority indicated that they liked and valued TBL, hence commitment to continuing to teach using the TBL method remains.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.110
GPT teacher head0.497
Teacher spread0.387 · 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