Reflective Practice for Professional Development Among Nursing Instructors
Bibliographic record
Abstract
Reflective practice among health professionals involves considering and questioning clinical experiences. The process of learning through work involves “reflection-in-action” (the skills of self-awareness, critical analysis, synthesis, and evaluation while executing clinical activities), and “reflection-on-action” which involves retrospective reviews of the clinical scenarios experienced by health professionals (Clouder, 2000; Duffy, 2009). Johns (1995) suggests that reflective practice is the professional’s ability to understand and learn from work experiences to achieve more effective and satisfying followup work experiences. Nursing instructors play a crucial role in helping nursing students consolidate taught theories and practice through guided and regular reflection on professional experiences (Duffy, 2009). To be effective guides, nursing instructors require the knowledge and skills necessary to implement reflective practice techniques into their teaching. This workshop actively engages participants in examining reflective practice by building on Gibbs’ (1998) six-step reflective cycle (i.e., description, feelings/thoughts, evaluation, analysis, conclusion, and action plan). The goal is to help instructors develop the necessary abilities to guide reflective practice among their students.
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 | 0.081 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".