Community health nurses leadership in advancing health equity
Bibliographic record
Abstract
OBJECTIVE: To describe the solutions community health nurses (CHNs) identify to address health inequities during the COVID-19 pandemic and to explore what leadership competencies enable CHNs to enact these solutions. DESIGN: Online survey, distributed to all members of the Community Health Nurses of Canada and associated provincial and territorial networks. PARTICIPANTS: Inclusion criteria included all nurses who were working during the COVID-19 pandemic in Canada. A total of 245 responses were included in the analysis. MEASUREMENT: The survey included 25 open ended and fixed response questions. Descriptive statistics were used to describe the quantitative data. Framework Analysis was used to analyze the qualitative data. RESULTS: Solutions focused on advancing health equity and expanding community relationships and partnerships were identified as priorities. To enact these solutions system transformation, engaging others, and developing coalitions were identified as the main leadership competencies required by CHNs. CONCLUSION: Participants in this study clearly articulated structural and process solutions to address health inequities among priority populations during the pandemic. CHNs described with practice knowledge and confidence that solutions enacted in system transformation with community partners are necessary to advance health equity.
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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 | 0.008 | 0.011 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".