Leadership Attributes: A Key to Optimal Utilization of the Community Health Nursing Workforce
Bibliographic record
Abstract
This research examined leadership attributes that support the optimal utilization and practice of community health nurses (CHNs). Community health nursing is facing challenges in workforce capacity and sustainability. To meet current and future demands on the community sector, it is essential to understand workplace attributes that facilitate effective utilization of existing human resources and recruitment of new nurses. This pan-Canadian, mixed-methods study included a demographic analysis of CHNs in Canada, a survey involving responses from approximately 6,700 CHNs to identify enablers and barriers to community health nursing practice and 23 focus groups to examine organizational attributes that "best" support optimal practice within the public health nursing subsector. Nursing leadership was identified as an important attribute in organizations' utilization and support of CHNs working to work effectively. This effectiveness, in turn, will enhance community health programs and overall healthcare system efficiency. This paper highlights findings related to the role of nursing leadership and leadership development in optimizing community health nursing practice.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".