Story Based Learning: A Student Centred Practice-Oriented Learning Strategy
Bibliographic record
Abstract
Story based learning (SBL) has evolved as a way to promote quality in nursing education by assisting faculty to develop a student-centred learning environment. SBL is a teaching/learning strategy that also strengthens learners' capacities to provide quality nursing care. Health professional education has been identified as a key contributor to advancing quality care. Key documents identify the pillars of quality health professional education as client–centred care, inter-professional education, teamwork and collaborative learning, knowledge mobilization and evidence-based practice, awareness of the limits of one’s knowledge as a foundation for reflective practice and life-long learning, and mastery of a field of practice. SBL incorporates elements of problem-based learning, case method teaching, and narrative pedagogy. The student-centred orientation of SBL aligns with the philosophical principles of client-centred nursing: respect for lived experience, participatory dialogue, and critical appraisal of health–related contexts. After providing an overview of SBL, we discuss the power of stories to engage learners in focused practice learning. We show how SBL sensitizes learners to: identify learning needs, develop information literacy, and recognize ethical, personal, interpersonal, and health team issues. We address how SBL fosters collaborative and participatory learning. Through a nursing lens learners using SBL identify a focus for nursing action, a process for negotiating nursing care, and appropriate nursing supports. The SBL process concludes with learners reflecting on what they have learned about learning and nursing. SBL is designed to develop in learners a habit of mind for clinical reasoning, reflective practice, and the delivery of quality nursing care.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".