Problem‐based learning in Guyana: a nursing education experiment
Bibliographic record
Abstract
AIM: This paper invites the reader into sharing a journey of change through a new curriculum grounded in a problem-based learning (PBL) approach to education in the first year of a diploma nursing programme in Guyana. BACKGROUND: In Guyana, students are trained using traditional teaching methods: lectures and a single, often outdated, text. The authors had been dissatisfied previously with their students' knowledge retention, critical thinking skills and application abilities. The authors became advocates for change through the introduction of a PBL approach in nursing education within their school. METHODS: PBL is quite different from 'problem solving', and the goal of learning is not to solve the problem, which has been presented. Rather, the problem is used to help students identify their own learning needs as they attempt to understand the problem, to pull together, synthesize and apply information to the problem, and to begin to work effectively to learn from group members as well as tutors. Students met in small groups to identify the problem; explore their pre-existing knowledge; generate hypotheses and possible mechanisms; and identify learning issues. CONCLUSION: Students in their first exposure to self-directed, small group learning can immediately thrive as active learners with minimal guidance and support. The programme was evaluated with the admission and scoring of homework/exams based on the school syllabus for the individual courses; and continual small group oral as well as a final written qualitative evaluation. Specific positive and negative learning factors are addressed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".