Community Health Nursing in Saudi Arabia: Practices and Learning Needs
Bibliographic record
Abstract
Purpose: This study investigated the daily practices of community nurses working in Primary Health Care Centers (PHCCs) and their learning needs. Participants and Methods: This descriptive cross-sectional correlational study was guided by the eight sections of the Canadian Community Health Nursing Standards of Practice 2019 expressing daily clinical activities and learning needs based on a five-point Likert scale. Participants were recruited from three Saudi Arabian cities. Descriptive data were processed regarding mean and Confidence Intervals for items, subscales, and the entire instrument. Comparisons for subgroups' mean scores were assessed using one-way ANOVA followed by a Tukey HSD pairwise comparison. Results: 318 nurses participated in the study (75.5% response rate). The top practiced nursing activities were health education and supporting those who are unable to take action for themselves. On the other hand, the least practiced were participating in research invitations and research groups. Participants expressed their learning needs in utilizing health education theories and strategies and using Health informatics to support optimum nursing care. Conclusion: Saudi Arabia has a young community health nursing taskforce that needs to upgrade its knowledge and skills to match international standards. Understanding the real-life activities and learning needs of community health nurses in comparison to international standards will help health policymakers support the optimal contribution of nursing to patients outcomes and healthcare system utilization.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".