Strategies for specialty training of healthcare professionals in low-resource settings: a systematic review on evidence from stroke care
Bibliographic record
Abstract
BACKGROUND: The greatest mortality and disability from stroke occurs in low- and middle-income countries. A significant barrier to implementation of best stroke care practices in these settings is limited availability of specialized healthcare training. We conducted a systematic review to determine the most effective methods for the provision of speciality stroke care education for hospital-based healthcare professionals in low-resource settings. METHODS: We followed the PRISMA guidelines for systematic reviews and searched PubMed, Web of Science and Scopus for original clinical research articles that described or evaluated stroke care education for hospital-based healthcare professionals in low-resource settings. Two reviewers screened titles/abstracts and then full text articles. Three reviewers critically appraised the articles selected for inclusion. RESULTS: A total of 1,182 articles were identified and eight were eligible for inclusion in this review; three were randomized controlled trials, four were non-randomized studies, and one was a descriptive study. Most studies used several approaches to education. A "train-the-trainer" approach to education was found to have the most positive clinical outcomes (lower overall complications, lengths of stay in hospital, and clinical vascular events). When used for quality improvement, the "train-the-trainer" approach increased patient reception of eligible performance measures. When technology was used to provide stroke education there was an increased frequency in diagnosis of stroke and use of antithrombotic treatment, reduced door-to-needle times, and increased support for decision making in medication prescription was reported. Task-shifting workshops for non-neurologists improved knowledge of stroke and patient care. Multidimensional education demonstrated an overall care quality improvement and increased prescriptions for evidence-based therapies, although, there were no significant differences in secondary prevention efforts, stroke reoccurrence or mortality rates. CONCLUSIONS: The "train the trainer" approach is likely the most effective strategy for specialist stroke education, while technology is also useful if resources are available to support its development and use. If resources are limited, basic knowledge education should be considered at a minimum and multidimensional training may not be as beneficial. Research into communities of practice, led by those in similar settings, may be helpful to develop educational initiatives with relevance to local contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".