Developing a Competence Framework and Evaluation Tool for Primary Care Nursing in South Africa
Bibliographic record
Abstract
CONTEXT: Nurses provide the bulk of primary care services in South Africa. Post-apartheid health legislation envisions the provision of comprehensive primary services at all public clinics, which implies the need for a cadre of primary care nurses able to render such services. OBJECTIVES: To identify core competencies of clinic nurses and develop an evaluation tool for primary care nursing in South Africa. METHODS: The descriptive and exploratory techniques used included two meetings of a reference group of South African primary care professionals, followed by a consensus-building exercise. Using the Delphi technique expert opinion was solicited from South Africa, Canada and the USA. FINDINGS: The reference group meetings yielded a list of nine core competencies. Infrastructure issues, such as the supermarket (one-stop shopping) approach to service delivery, communication and transport systems, and the quality of supervision still cause concern. These issues underscore that competence cannot be measured in a vacuum. Input from Delphi participants affirmed the nine core competencies and the need to assess the impact of core competency training. One possible way to measure the nine core competencies would be to use proxy indicators. DISCUSSION/CONCLUSIONS: Identifying core competencies is a complex process. There is a need to process a wide range of views and ideas. Also, balancing academic concerns with service delivery needs and constraints is an ongoing challenge. A potential limitation of the Delphi technique is participant selection bias and fatigue. Accessing a diverse international panel and making numerous follow up attempts via phone, mail and email were used to attempt to ameliorate these inherent limitations. Although the process is cumbersome, providing "experts" with a venue to wrestle with these ideas can be fruitful. Future studies would help to assess the reliability of the findings.
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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 | 0.039 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".