Community health nurses’ learning needs in relation to the Canadian community health nursing standards of practice: results from a Canadian survey
Bibliographic record
Abstract
BACKGROUND: CANADIAN COMMUNITY HEALTH NURSES (CHNS) WORK IN DIVERSE URBAN, RURAL, AND REMOTE SETTINGS SUCH AS: public health units/departments, home health, community health facilities, family practices, and other community-based settings. Research into specific learning needs of practicing CHNs is sparsely reported. This paper examines Canadian CHNs learning needs in relation to the 2008 Canadian Community Health Nursing Standards of Practice (CCHN Standards). It answers: What are the learning needs of CHNs in Canada in relation to the CCHN Standards? What are differences in CHNs' learning needs by: province and territory in Canada, work setting (home health, public health and other community health settings) and years of nursing practice? METHODS: Between late 2008 and early 2009 a national survey was conducted to identify learning needs of CHNs based on the CCHN Standards using a validated tool. RESULTS: Results indicated that CHNs had learning needs on 25 of 88 items (28.4%), suggesting CHNs have confidence in most CCHN Standards. Three items had the highest learning needs with mean scores > 0.60: two related to epidemiology (means 0.62 and 0.75); and one to informatics (application of information and communication technology) (mean = 0.73). Public health nurses had a greater need to know about "…evaluating population health promotion programs systematically" compared to home health nurses (mean 0.66 vs. 0.39, p <0.010). Nurses with under two years experience had a greater need to learn "… advocating for healthy public policy…" than their more experienced peers (p = 0.0029). Also, NPs had a greater need to learn about "…using community development principles when engaging the individual/community in a consultative process" compared to RNs (p = 0.05). Many nurses were unsure if they applied foundational theoretical frameworks (i.e., the Ottawa Charter of Health Promotion, the Jakarta Declaration, and the Population Health Promotion Model) in practice. CONCLUSIONS: CHN educators and practice leaders need to consider these results in determining where to strengthen content in graduate and undergraduate nursing programs, as well as professional development programs. For practicing CHNs educational content should be tailored based on learner's years of experience in the community and their employment sector.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".