The drivers of evidence-based practice (EBP) at inception: Implications for low and medium-income countries (LMICS)
Bibliographic record
Abstract
Low and medium-income countries (LMICS) desire the multiple benefits of EBP but have achieved minimal success so far. Moreso, frameworks for implementing EBP fail to acknowledge the external socio-political factors as core component of uptake and sustaining EBP in health care settings. Consequently, this paper will examine the influence of drivers of EBP and the implications for sustaining EBP diffusion into LMICS. Theoretically, EBP proposes that clinical treatment decisions be based on the most current verifiable evidence. Associated with improved quality of care, EBP is the universal standard of clinical interventions. Yet, since introducing EBP to LMICS, the integration process has been slow compared to the rapid development witnessed in the UK, US and Canada over the past 30 years. EBP proponents linked the resistance with institutional barriers in the LMICS. However, this paper argues that the external socio-political context is a proven barrier-breaking force but presently underestimated in LMICS. Central to this review, the socio-political dynamics in the UK, US and Canada were discussed to mirror the powerful influence that propelled EBP at its successful inception. The implication is that breaking the institutional barriers against sustainable EBP implementation in the LMICS requires synergy of influential forces outside the hospital settings. Unfortunately, most implementation studies from LMICS are limited to institutional barriers. Finally, hospital settings in LMICS face unique problems integrating EBP with daily care and to overcome the barriers, closer attention must be paid to the influence of the surrounding factors; political, technological, managerial and globalisation forces.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".