An exploratory study of nurses' perceptions of their action learning experience /
Bibliographic record
Abstract
Each year, the College of Nurses of Ontario (CNO) requires all registered \nnurses and registered practical nurses in Ontario to complete a Reflective Practice \nlearning activity. In doing so, nurses are expected to perform a self- assessment, \nidentify a practice problem or issue, create and implement a personal learning plan, \nand evaluate the learning and outcomes accomplished. The process and components \nof CNO's Reflective Practice program are very similar to an Action Learning \nactivity. \nThe purpose of this qualitative research was to explore the perceptions of 1 \n1 \nnurses who completed at least 1 Action Learning activity. Data analysis of their \ncomments provided insight into their perceptions of the Action Learning experience, \nperceptions of the negative and positive characteristics of various activities within \nthe Action Learning process, and perceptions of barriers or challenges within this \nexperience. The author concluded that participants perceived their Action Learning \nactivities to be a positive experience because the process focused on practice \nproblems and issues, enhanced thinking about practice problems, and achieved \npractice-relevant outcomes. However, the results indicated that self-directed \nlearning and journal writing were difficult activities for some participants, and some \nexperienced negative emotional responses during reflection. The research \nconcluded that barriers to implementation of Action Learning include a lack of \nunderstanding of the process and a perceived lack of support from employers.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".