The Importance of Continuing Professional Development to Career Satisfaction and Patient Care: Meeting the Needs of Novice to Mid- to Late-Career Nurses throughout Their Career Span
Bibliographic record
Abstract
This paper provides insights into the role of ongoing training and education on nurses’ career satisfaction across different career stages and their ability to provide quality patient care. Eighteen focus groups were conducted over the course of five months in 2015 (January to May) in eight Canadian provinces. There were a total of 185 focus group participants. Each focus group lasted approximately 1.5 h and included 8–15 participants who self-selected in one of three distinct career stages (students, early-career, mid- to late-career). A thematic analysis of the data revealed that ongoing professional development is an expressed need and expectation for nurses across the various career stages. Student and early-career nurses expected sufficient training and education to facilitate workplace transitions, as well as continuing education opportunities throughout their careers for career laddering. For mid- to late-career nurses, the importance of lifelong learning was understood within the context of maintaining competency, providing quality patient care and enhancing future career opportunities. Training and education were directly linked to nurses’ career satisfaction. Healthy work environments were identified by nurses as those that invested in continuing professional development opportunities to ensure continuous growth in their practice and provide optimal quality patient care. Training and education emerged as a cross-cutting theme across all career stages and held implications for patient care, as well as retention and recruitment.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".