Much at stake: the importance of training and capacity building for stakeholder engagement in evidence synthesis

Abstract

Systematic reviews and maps are complex methods for synthesising evidence that involve specialist and resource-intensive activities. Systematic reviewers face challenges when attempting to clearly and precisely communicate their methods to end-users and other stakeholder groups. We propose that these challenges are likely to be a key causal factor in the generally low uptake of systematic reviews and maps by policy and practitioners in environmental science and management. We argue that training and capacity building are inherently important components of systematic reviews and maps for all stakeholders; the reviewers themselves, the end-users of specific reviews, and the broader research and decision-making community. Training can help to build capacity for undertaking reviews and maps, and can help to explain complex methods to stakeholders. Training is important for those wishing to undertake stakeholder engagement activities as part of a review. It allows researchers and decision-makers to critique systematic reviews and maps based on their methods. Finally, training may be necessary to allow reviewers to prepare visualisations and communication media for presenting the findings of systematic reviews and maps. We conclude that a broad approach, by viewing every opportunity of stakeholder engagement as a potential for training and capacity building is appropriate both within a specific review and across reviews as a community of practice in evidence synthesis. We call for systematic reviewers to improve networks across disciplines in relation to training, sharing experiences and course content, and ensuring a consistent approach to capacity building in the conduct and use of evidence syntheses.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 14 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.