Community health assessment of needs and continuous empowerment (CHANCE): a quantitative cross-sectional survey targeting primary health care nurses in Lebanon
Bibliographic record
Abstract
BACKGROUND: Primary Health Care (PHC) is the cornerstone of any healthcare system, with nurses forming the largest workforce involved in care. This study aimed to assess the current use of core competencies among community-based nurses, identify their learning needs, and assess factors associated with training needs within PHC centers. METHODS: A quantitative cross-sectional survey design was used, targeting community health nurses working within primary healthcare centers. Data were collected using a survey instrument adapted from the Canadian Community Health Nurses' Standards of Practice and informed by a validated tool, then piloted for clarity in the Lebanese context. Data were collected between September and November 2018. Mean, standard deviation (SD), frequency, and percentage data were computed for descriptive purposes. The generalized estimating equation (GEE) was used to identify the factors associated with nurses' training needs clustered within centers. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using logistic GEE regression models that accounted for cluster effects. RESULTS: The total number of PHCs that agreed to participate was 206, of which 173 returned completed surveys. Given that we do not have an accurate number of the population of nurses working in those centers, we assumed that there would be two nurses in each PHC. Thus, for a total of 251 surveys completed by nurses, the response rate is estimated to be 61%. Of the 173 surveys, 139 were included in the final analysis after deleting those that were incomplete. Descriptive results showed that nurses were competent in providing continuous care (60.0%), electronic technology use (55.08%), and clinical nursing assessment (54.01%). They reported a need for more training on community health promotion (65.12%), patient-centered care (PCC) (58.30%), and patient self-management of chronic diseases (52.0%). In comparison to nurses working in accredited centers, nurses working in centers in the process of becoming accredited required three times more training to become competent in PCC (OR = 3.39, 95% CI: 1.26-9.31, p = 0.016). Registered nurses required three times less training in PCC than senior/head nurses (OR = 0.30, 95% CI: 0.11-0.80, p = 0.016). Education level was statistically significantly associated with most training needs. Nurses with Baccalaureate and Technique Superior degrees needed six times more training (OR = 6.07, 95% CI: 1.81-31.16, p = 0.031) than those with a bachelor's or master's degree in nursing. CONCLUSION: This study provided a baseline assessment for the competencies that nurses reported implementing and those that they requested more training on. Future steps would be to develop interventions to empower nurses with the competencies they requested as priorities and to conduct a post intervention assessment to test the effect of the training on nursing adoption of those skills.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".