Factors Enabling and Constraining CPD compliance amongst South African Dental Technicians practising in KwaZulu-Natal, South Africa.
Bibliographic record
Abstract
Health professions’ regulatory bodies are experiencing numerous challenges with compliance to Continuing Professional Development (CPD) requirements. The South African Dental Technicians Council (SADTC) stipulates that dental technicians be CPD compliant and accrue an annual minimum of 30 Continuing Educational Units (CEUs). The SADTC acknowledged that there is a lack of compliance with CPD by dental technicians. The study aimed to elicit dental technicians’ opinions on, and experiences of, continuing professional development. The study utilized a descriptive cross-sectional research design within a quantitative framework. A purposeful sampling technique was used to select and invite registered dental technicians (n=103) from KwaZulu-Natal (KZN).Dental technicians (n =103) in KZN were invited to participate in the study by completing an online questionnaire, which elicited their experiences with regards to meeting their CPD requirements. Dental technicians preferred formal, employer-funded CPD activities that are conducted during working hours as compared to online CPD activities. Dental technicians acknowledged that mandatory CPD is a costly requirement. They further recognised that they were unaware of non-attendance based CPD activities as methods of accruing CEUs. The study revealed that dental technicians in KZN experience challenges in being CPD compliant.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".