Factors associated with clinical practice competency among nursing and health science students in Ethiopia
Bibliographic record
Abstract
Background: Clinical practice competence is affected by different factors in a clinical setting like the skill of the educator, staff–student interaction, and a clear assessment guideline. Effective mentoring and constructive feedback will also influence learning. Poor performance is caused by low competence and improving competency should improve performance. The purpose of this meta-analysis is to assess factors affecting the clinical practice competency of medical and health science students in Ethiopia. Methods: We conducted a related literature search (February up to March 2023) of PubMed and Web of Science databases for studies describing the factors associated with clinical practice competency among medical and health science students in Ethiopia. The quality of studies was independently assessed by the Newcastle–Ottawa quality scale (NOS), which was guided by the PRISMA checklist. The Q test and I2 statistics were used to evaluate the heterogeneity among selected studies. If the heterogeneity was obvious (I2>50%), the random effects model (REM) was used. If the heterogeneity was low (I2≤50%), the fixed effects model (FEM) was used. Results: There were 1613 participants in four (4) investigations. The pooled effect size of clinical practice competency among students in the form of odds ratio (OR) with the presence of a checklist was 3.40 (95% CI 2.50–4.62), p<0.01, I2=0%), with the orientation of objective was 3.84 (95% CI 2.29–6.43), p<0.00001, I2=57%), having confidence during performing the procedure was 2.16 (95% CI 1.17–3.99), p=0.01, I2=53%). The final pooled effect size after trim and fill analysis in the random effect model was found to be 1.27 (95%CI: -0.19, -2.73) for the association between staff encouragement to do practice and clinical practice competency. This indicated that absence of a significant association between staff encouragement to do practice and clinical practice competency among medical and health science students in Ethiopia. Conclusions: The presence of a checklist, the orientation of objective, and students having confidence while performing a procedure are factors associated with clinical practice competency among nursing and health science students in Ethiopia.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".