Experiential Learning Curriculum Delivery Approach for Quality Improvement in Resource Limited Settings: Mobile Learning for Point-of-Care Technologies
Bibliographic record
Abstract
BACKGROUND: Despite impressive progress that has been made in the provision of health care services to all, the issue of quality service delivery still remains a challenge particularly for point-of-care (POC) diagnostics in resource-limited-settings. Poor competency of primary health care workers in these settings has been shown to be amongst the main contributors to poor quality service delivery. FINDINGS: Participatory-based continuous professional development (CPD) strategies to support technology advancements in health care are recommended. Experiential learning approaches have been shown to be efficient in supplementing traditional teaching methods for both health care students and professionals. These approaches have been shown to further contribute towards continuous skills development and lifelong learning. CONCLUSION: This review therefore provided an overview of literature on experiential learning as one of CPD approaches in relation to health care service improvement in resource-limited setting. In addition, this review has recommended a mobile-based experiential learning approach to help deliver a quality POC technology curriculum to Primary health care-based workers in resource-limited settings.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".