Influences Shaping Nurses’ Continuing Professional Education Choices and Learning Pathways: An Exploratory Case Study
Bibliographic record
Abstract
BackgroundNurses engage in continuing professional education (CPE) to remain current in their knowledge and competencies, and to learn in ways that help them navigate an increasingly complex healthcare system. Recent trends indicate that CPE has shifted away from nurses' education to be more directed toward organizational and regulatory needs, which impacts nurses' professional learning.PurposeThe purpose of this research was to understand the influences that shape nurses' CPE choices and professional learning pathways, and the ways in which nurses learn.MethodsThis was an exploratory case study of later career nurses in Nova Scotia, Canada, that analyzed data from semi-structured interviews, participant artifacts, and government and regulatory policy documents. Critical and post-structural feminist lenses were applied to the data analysis.ResultsThree themes encompassing the key influences on nurses' CPE choices and learning pathways were identified: sociocultural context, structural/systems context, and shifting knowledge forms. The findings suggest that educational discourse embedded in the broader regulatory, government, and employer policy worked to direct the participants into CPE for employment and regulatory requirements, shaping nursing knowledge that reflects organizational needs.ConclusionThis study revealed sources of influence on nurses' CPE choices and professional learning pathways, such as sociocultural expectations for women to assume most family responsibilities. Structural influences within healthcare and regulation exert considerable influence on nurses' CPE and learning pathways to align with system needs. This study highlighted the limitations of these influences and the need for CPE programs and learning for nurses that enable rather than constrain their continued professional development.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| 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".