Continuing education needs of registered nurses in Northwestern Ontario : a needs assessment approach / Laura Kokocinski.
Bibliographic record
Abstract
In Canada, there has been little progress in the \ndevelopment and advancement of continuing nursing \neducation since World War II. The recent release of \nnational and provincial documents, the trend toward \nspecialization and the need for increased knowledge based \non technological changes in practice, supports the \nimportance of continuing nursing education and its vital \nrole in maintaining the credibility of the profession. \nThe purpose of this study was to investigate and \ndescribe the continuing education needs of registered \nnurses in Northwestern Ontario. A Needs Assessment?s \nFramework for Continuing Nursing Education was developed \nto answer the research questions. The research design \ninvolved mailing a questionnaire to 800 registered nurses \nin Northwestern Ontario and personal interviews with ten \nparticipants. Data was analyzed using descriptive and \ncorrelation statistics. \nContinuing education needs were defined, as well as \nthe factors which influence these needs. The findings \nindicated that nurses in Northwestern Ontario were \ninterested in pursuing continuing education. However, the \nmajority were not currently participating in continuing education. The results suggest that geographical \nlocation, educational preparation, area of employment and \nmotivational considerations affect continuing education \nneeds and are predictors of participation. Other \nvariables, such as; "valuing" of continuing education, \nthe need for professional upgrading, accessible \neducational offerings, educational delivery methods, the \nvariety of educational needs and the busy lifestyles of \nthe nurses surveyed, suggest trends or influences which \nmay impact the continuing education needs of registered \nnurses in Northwestern Ontario. \nThe present study also has several implications for \ncontinuing nursing education in Northwestern Ontario. The \nfindings indicate that continuing education must be \nrecognized and valued by nurses, employers and the \nprofession as a whole. Educational offerings could then \nbe planned and offered in a collaborative approach to \nmeet the nurses needs. The results of this study also \nindicate that further research is required to explore \nmotivational orientations, appropriate learning methods, \nbarriers to participation, and to investigate the \neffectiveness of the voluntary model for continuing \neducation.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".