Describing Clinical Nurse Specialist Practice: A Mixed-Methods Study
Bibliographic record
Abstract
PURPOSE/AIMS: To describe clinical nurse specialist practice in Québec, Canada, and propose a dashboard to track role dimensions and outcomes. DESIGN: Sequential mixed-methods study across 6 sites in Québec (June 2021 to May 2022). METHODS: Phase 1: Focus groups (n = 8) and individual interviews (n = 3) were conducted to adapt a time and motion tool. Phase 2: Time and motion studies (n = 25; 203 hours 5 minutes) described clinical nurse specialist practice. Phase 3: A rapid literature review and study participants' feedback informed the dashboard's development. Analysis: Descriptive statistics, with content analysis for qualitative data. RESULTS: The proportion of time clinical nurse specialists spent in role dimensions included clinical (22.8%), education (11.2%), administrative/leadership (48.6%), research (9.6%), and personal (7.7%). On average, they spent 17% of work time with patients, but this varied across specialties and locations. Key dashboard characteristics and uses were identified. CONCLUSIONS: Important differences were noted in clinical nurse specialist time spent in activities across specialties and regions in Québec. Approximately one-fifth of work time was spent in direct patient care. Additional research is needed to examine the link between clinical nurse specialist practice and outcomes in other jurisdictions and test the implementation of a dashboard to make their practice more visible.
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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 | 0.023 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".