Continuing professional development (CPD) system development, implementation, evaluation and sustainability for healthcare professionals in low- and lower-middle-income countries: a rapid scoping review
Bibliographic record
Abstract
Abstract Background Policymakers and program developers in low-and lower-middle-income countries (LLMICs) are increasingly seeking evidence-based information and guidance on how to successfully develop and implement continuing professional development (CPD) systems. We conducted a rapid scoping review to map and synthesize what is known regarding the development, implementation, evaluation and sustainability of CPD systems for healthcare professionals in LLMICs. Methods We searched MEDLINE, CINAHL and Web of Science. Reference lists were screened and a cited reference search of included articles was conducted. Supplementary information on the CPD systems identified in the articles was also identified via an online targeted grey literature search. English, French and Spanish literature published from 2011 to 2021 were considered. Data were extracted and combined and summarized according to country/region and healthcare profession via tables and narrative text. Results We included 15 articles and 23 grey literature sources. Africa was the region most represented followed by South and Southeast Asia and the Middle East. The literature most often referred to CPD systems for nurses and midwives; CPD systems for physicians were frequently referred to as well. Findings show that leadership and buy-in from key stakeholders, including government bodies and healthcare professional organizations, and a framework are essential for the development, implementation and sustainability of a CPD system in a LLMIC. The guiding framework should incorporate a regulatory perspective, as well as a conceptual lens (that informs CPD objectives and methods), and should consider contextual factors (support for CPD, healthcare context and population health needs). In terms of important steps to undertake, these include: a needs assessment; drafting of a policy, which details the regulations (laws/norms), the CPD requirements and an approach for monitoring, including an accreditation mechanism; a financing plan; identification and production of appropriate CPD materials and activities; a communication strategy; and an evaluation process. Conclusion Leadership, a framework and a clearly delineated plan that is responsive to the needs and context of the setting, are essential for the development, implementation and sustainability of a CPD system for healthcare professionals in a LLMIC.
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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 | 0.103 | 0.243 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".