Mapping Evidence of Experiential learning for Primary Health Care Workers in Low-and-Middle-Income Countries: A Scoping Review
Bibliographic record
Abstract
Background: Improving equity of healthcare is one of the main global health priorities, particularly in low and medium-income-countries (LMICs). However, most LMICs still struggle to achieve equity in healthcare provision. One of the major contributing factors to achieving health equity in these settings include low levels of healthcare competency among primary healthcare workers. Experiential learning has been shown to contribute effectively to healthcare curriculum delivery. The purpose of this study was to systematically map evidence of experiential learning training programs for PHC workers with a focus on quality improvement in LMICs. Findings: Of the 240 022 articles retrieved from database search, 129 studies were found to be eligible for inclusion in abstract screening following title screening. Subsequent to abstract screening, 29 articles were eligible for inclusion in full article screening. Full article screening resulted in four articles found eligible for inclusion for data extraction and quality appraisal. Included studies were conducted in the following Countries: South Africa, China and Brazil. The following themes emerged: The utility, efficiency and acceptability of experiential learning approaches to PHC workers and Reflection. Experiential learning through various approaches was shown to have the potential to provide an important practical aspect on curriculum delivery not easily taught in lecture-based learning. Skills developed by PHC students in LMICs included communication, empathy, creativity and critical-reflexive skills. The reflection step of experiential learning was shown to be a useful tool to identify root causes of health systems inefficiencies and to inform policy making. The quality of included studies was found to range from above average to high quality. Conclusion: Limited research on the utility, efficiency and acceptability of experiential learning approaches to PHC-based professionals as well as on the impact of these approaches to the provision of quality services was found. Research focused on the development and piloting of experiential learning approaches to determine feasibility and to ensure effectiveness of interventions towards continuous professional development and life-long learning of PHC nurses in rural clinics is recommended.
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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 | 0.030 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".