Self-Study: A Method for Continuous Professional Learning and A Methodology for Knowledge Transfer
Bibliographic record
Abstract
Purpose: Clinical nursing instructors must participate in continuous professional learning to maintain competency in nursing practice and in clinical instruction to prepare nursing students adequately for professional practice. The purpose of this research was to examine self-study as a method of continuous professional learning in nursing education. Procedures: A clinical instructor undertook to improve her clinical instruction with regard to five formative assessment strategies illustrated to promote student learning in regular classrooms. She translated and then implemented these strategies in nursing education. This self-study of instructional practice employed a reflective journal and systematic documentation of iterative processes of planning, action, reflection, and decision-making to examine the use of these strategies with nursing students. Findings: Self-study was demonstrated to be effective as an approach for continuous professional learning. Guidance for employing self-study to support a clinical instructor’s professional learning outlines four steps: provoking ideas, describing implementation, reflecting on implementation and the consequences of practice, and making decisions for moving forward. It was found that self-study facilitated the transfer of knowledge about formative assessment from classroom education research to nursing education and supported knowledge transfer from one teaching discipline to another. Conclusions: Self-study was an effective method for personal and professional growth and development pertaining to formative assessment in nursing education. As a methodology, self-study facilitated knowledge transfer between professions. This is the first known self-study of pedagogical practice in clinical nursing education.
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 | 0.087 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".