Developing and Sustaining Leadership in Public Health Nursing: Findings from One British Columbia Health Authority
Bibliographic record
Abstract
OBJECTIVE: To develop clinical leadership among front-line public health nurses (PHNs). METHODS: This paper describes a quality improvement process to develop clinical leadership among front-line PHNs. Three activities were undertaken by a working group consisting mainly of front-line staff: engaging PHNs in an online change-readiness questionnaire, administering a survey to clients who had ever used public health services delivered by one Vancouver Community Infant, Child and Youth (ICY) program team and conducting three group interviews with public health providers. The group interviews asked about PHN practice. They were analyzed using thematic content analysis. RESULTS: This quality improvement project suggests that PHNs (n=70) strongly believed in opportunities for system improvement. Client surveys (n=429) and community partner surveys (n=79) revealed the importance of the PHN role. Group interview data yielded three themes: PHNs were the "hub" of community care; PHNs lacked a common language to describe their work; PHNs envisioned their future practice encompassing their full scope of competencies. PHNs developed the "ICY Public Health Nursing Model," which articulates 14 public health interventions and identifies the scope of their work. CONCLUSION: Developing and sustaining clinical leadership in front-line PHNs was accomplished through these various quality assurance activities.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".