A Continuous Professional Development Strategy for Expanded Competencies Needed by Radiographers Working in Rural Areas
Bibliographic record
Abstract
INTRODUCTION: The emphasis on Primary Health Care (PHC) with a focus on preventative care offers a challenge for rural radiographers to advance solutions that are change focused. Published evidence suggest that allied health professionals such as radiographers employed in rural areas of South Africa were confronted with an assortment of challenges and responsibilities that demand a wide range of skills and competencies. Additional skills could be essential and Continuous Professional Development (CPD) strategy could be used as a vehicle to equip rural radiographers. OBJECTIVE: To propose a CPD strategy that may support rural radiographers’ expanded and extended competency development needs. METHODS: This research used exploratory sequential study design involving Phase I (qualitative) and Phase II (quantitative) with seven participants and 101 respondents respectively. The CPD strategy development was based on the results from data analysis of both strands. Since strategy development is based on a process of trustworthiness, six evaluators from the clinical and academia were consulted. The evaluators were purposely selected. RESULTS: A final CPD strategy for rural radiographers was proposed. Results from a mixed method study were used in the process of developing the CPD strategy. DISCUSSION: Radiographers working in rural areas of KwaZulu Natal (KZN) a province in South Africa are faced with emerging competency need that require both extended and expanded competencies which may be beyond those required for professional registration. This unmet competency needs can be supported by a CPD strategy that is aligned to these competency needs.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".