Clinical Nurse Specialists in Canada: Why Are Some Not Working in the Role?
Bibliographic record
Abstract
Clinical nurse specialists (CNSs) are advanced practice nurses. They contribute to the quality and safety of patient care by providing an advanced level of clinical care to patients and families and by supporting healthcare team members to deliver evidence-based care. CNSs help to reduce healthcare costs when the roles are fully deployed and all the dimensions of the CNS role are implemented. The dimensions of the CNS role include clinical care, organizational leadership, research, education, professional development and consultation to provide patient care. There is a paucity of research on CNSs in Canada. We conducted the first Canada-wide survey of CNSs and asked each nursing regulatory body to identify the CNSs in their registration database. One-quarter (n=196/776) of the regulator-identified CNS respondents whom we contacted for the study were no longer or had never been a CNS. Currently, adequate mechanisms are lacking to identify and track CNSs in Canada, and little is known about the factors that influence CNSs' decisions to leave their role. The non-employed CNS respondents in our survey highlighted that the lack of role clarity, their inability to find employment as a CNS and the inability to implement all the dimensions of the CNS role were key factors in their decision not to work as a CNS. These findings have important implications, given that these factors are potentially modifiable and amenable to decisions made by nursing leaders in organizations and regulatory bodies. Mechanisms to identify and track CNSs in Canada are needed to develop an effective workforce plan and maximize the integration of CNSs in the workforce.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".