Systematic Reviews and Knowledge translation/Revues Systematiques et Mise En Pratique Des connaissances/Revisiones Sistematicas Y Traslacion De Conocimientos
Bibliographic record
Abstract
Introduction Although proven effective interventions exist that would enable all countries to meet Millennium Development Goals, (1) uptake and use of these interventions among poorest populations is at least 50% less than among richest populations within each country. (2) Furthermore, we have recently shown that community effectiveness of interventions is lower in poorest populations owing to a effect of lower coverage and/or access, inferior diagnostic accuracy, less provider compliance and less consumer adherence. (3) The WHO Knowledge Management and Sharing (KMS) group adapted Canadian Institutes of Health Research definition of knowledge translation (4) (KT) for lower- and middle-income countries (LMIC) as: the synthesis, exchange and application of knowledge by relevant stakeholders to accelerate benefits of global and local innovation in strengthening health systems and improving people's health. (5) In addition to this focus on health systems, we propose that KT strategies aiming to enhance equity need to target barriers to achieving optimal effectiveness across socioeconomic status (SES). Although systematic reviews are increasingly recognized as best available source of evidence for decisions about health-care management and policy, owing to greater confidence and less bias in results than when relying on individual trials, (6-8) they have tended to focus on average results, ignoring distributional effects that are likely to occur in implementing these interventions. (9) Expanding on our recently-published equity-effectiveness loop (Fig. 1) framework, (3) we propose an evidence-based framework--or cascade--for equity-oriented knowledge translation (Fig. 2), drawing on systematic reviews to assess barriers and facilitators, identifying interventions to overcome barriers, choosing appropriate KT strategies, evaluation, through to knowledge management and sharing. We use two tracer interventions to illustrate this framework. [FIGURES 1-2 OMITTED] Methods Equity-effectiveness KT initiatives should be saved for interventions of known efficacy that are documented by systematic reviews. We selected two such interventions of major importance in LMIC--insecticide-treated bednets (ITNs) and immunization--and used community equity-effectiveness loop (Fig. 1) to assess community effectiveness across equity factors as a first step to identifying key barriers that need to be addressed by KT strategies to enhance health equity. (3) We used hypothetical estimates to estimate equity-effectiveness (Table 1). We have not assessed impact on diagnostic or screening accuracy, since all individuals are eligible for both ITNs and immunization. ITNs A Cochrane systematic review found that efficacy of ITNs in reducing mortality from malaria is 20%. (10) This potential is attenuated by a effect of barriers, i.e. incomplete access (availability, affordability), partial provider compliance in recommending ITNs, and incomplete consumer adherence in using bednet once purchased; leading to a loss of more than half potential benefit, with greater loss in poorest (see Table 1). Immunization The efficacy of immunization against childhood diseases has been estimated at greater than 80%. (11) Again, owing to critically important barriers, downward staircase effect dramatically reduces true impact: full immunization is achieved for only 40% of poorest economic quintile compared with about 60% of richest in 56 countries. Access to immunization depends on setting. In most of Africa, immunization is offered free-of-charge by Expanded Programme on Immunization (EPI), but access is imperfect owing to system constraints such as supply and production issues, human resources and organizational constraints. Provider compliance (defined as attention to cold-chain and providers' willingness and ability to comply with recommended immunization schedule) and consumer adherence (defined in this context as willingness of general public to immunize) were estimated to be 80% for richest and 70% for poorest populations, respectively. …
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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 | 0.449 | 0.682 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.010 |
| Bibliometrics | 0.049 | 0.042 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".