Validation of a clinical competence evaluation tool for community service nurses in North West province, South Africa
Bibliographic record
Abstract
BACKGROUND: Little has been done to evaluate clinical competence of community service nurses (CSNs) during the 12-month compulsory community service in South Africa. Evaluating clinical competence of CSNs would be of benefit as it might improve quality patient care and promote patient satisfaction. It therefore became of paramount importance for the researcher to establish some method of evaluating the CSNs' clinical competence during their compulsory service in the North West province (NWP), South Africa. AIM: To evaluate the clinical competence evaluation tool (CCET) for CSNs for reliability and validity. SETTING: A selected regional level 2 hospital. METHODS: Ten experts participated in the validation process. The tool was tested at one of the public hospitals in the NWP and 11 out of 13 CSNs participated in this process. Statistical Package for the Social Sciences version 25 was employed and the reliability of the tool was measured using Cronbach's alpha. RESULTS: This tool's content validity index has exceeded 0.80 and is indicated at 0.98, which reflects excellent content validity. The higher the content validity ratio score the greater the agreement amongst the experts. The Cronbach's alpha coefficients in the six competencies are all greater than 0.7 implying that the tool developed in this study is reliable. All the experts indicated that the tool is clear, simple, general, accessible and important. CONCLUSION: From the above-mentioned results, a CCET for CSNs was proven to be valid and reliable. CONTRIBUTION: This was the first tool to be developed in NWP of South Africa.
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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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".