Effects of problem-based learning on nurse competence: A systematic review
Bibliographic record
Abstract
Objective : The aim of this review was to examine studies for evidence of the effects of problem-based learning on the competence of nurses in clinical practice. Methods : A 5-step systematic review was undertaken as follows: defining the review question, setting the review objectives, searching databases to identify relevant studies between 1999-2009, selecting studies according to set criteria, and extracting and analysing the data. A primary review of 2,815 abstracts led to the selection of 11 studies, identified from a search of eight databases. By consensus review these were narrowed down to five studies: one quantitative and four qualitative. Using the Joanna Briggs SUMARI (System for the Unified Management, Assessment and Review of Information) programme, data were analyzed by meta-synthesis of the qualitative studies and a narrative summary of the quantitative study. Results : Five studies (two from the USA; two from South Africa; one from Canada) met the inclusion criteria. From the evidence it was found that problem-based learning (PBL) had positive effects on nurse competence. The most commonly identified competencies include problem-solving, critical thinking, self-directedness and independent practice. PBL is instrumental in equipping nurses with leadership skills and the ability to provide high level, quality patient care. Conclusions : Problem-based learning has positive effects on the development of nurse competence. Supervisors in clinical practice are generally positive about graduates’ competence and are inclined to place them in a leadership position in clinical areas.
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 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 | 0.021 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".