Evaluating the effectiveness of continuing professional development training program: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Continuing Professional Development (CPD) is essential for enhancing the competencies of healthcare professionals in healthcare settings. Without continuous training, quality of services and patient safety might be at risk. METHODS: This quantitative retrospective cohort study analyzed paired pre- and post-test data from 538 participants across three independent CPD (Healthcare Quality and Risk Management (HQRM), Central Sterile Supply Department (CSSD), Accident Prevention for Healthcare Professionals (APHP)) delivered over 48 sessions between 2023 and 2024. Data on participant attendance, knowledge acquisition (self-assessment ratings and pre- and post-course examinations), and participant survey feedback post courses were analyzed. RESULTS: The analysis showed that around 46.7% of participants were nurses, 36.8% allied health practitioners, 9.7% physicians, 4.7% dentists and 2.3% pharmacists, with 97.4% coming from governmental institutions. The participants’ self-perception of the courses, based on the self-assessment questionnaire, indicates a strong belief in the courses’ value and impact. All three courses resulted in highly statistically significant knowledge acquisition (p < .001). The mean pre-test scores improved by 38.3 to 67.1% points. Overall satisfaction was high, and participants self-reported improved competence with ratings above 96%. Key themes gathered from qualitative assessment revealed that participants plan to further professional development, implementation of new strategies in their work, and continued self-assessment to monitor progress. Future CPD topics suggested were advanced clinical skills, patient-centered care, telemedicine, and the integration of technology in healthcare. CONCLUSIONS: The courses were perceived as relevant to professional practice and associated with higher post-test knowledge scores. Future research, incorporating longitudinal follow-up is warranted to establish the definitive causal link between this training and sustained improvements in professional practice.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".