Dataset Information

Assessing the Claims of Participatory Measurement, Reporting and Verification (PMRV) in Achieving REDD+ Outcomes: A Systematic Review.

ABSTRACT: Participation of local communities in the Measurement, Reporting and Verification (MRV) of forest changes has been promoted as a strategy that lowers the cost of MRV and increases their engagement with REDD+. This systematic review of literature assessed the claims of participatory MRV (PMRV) in achieving REDD+ outcomes. We identified 29 PMRV publications that consisted of 20 peer-reviewed and 9 non peer-reviewed publications, with 14 publications being empirically based studies. The evidence supporting PMRV claims was categorized into empirical finding, citation or assumption. Our analysis of the empirical studies showed that PMRV projects were conducted in 17 countries in three tropical continents and across various forest and land tenure types. Most of these projects tested the feasibility of participatory measurement or monitoring, which limited the participation of local communities to data gathering. PMRV claims of providing accurate local biomass measurements and lowering MRV cost were well-supported with empirical evidence. Claims that PMRV supports REDD+ social outcomes that affect local communities directly, such as increased environmental awareness and equity in benefit sharing, were supported with less empirical evidence than REDD+ technical outcomes. This may be due to the difficulties in measuring social outcomes and the slow progress in the development and implementation of REDD+ components outside of experimental research contexts. Although lessons from other monitoring contexts have been used to support PMRV claims, they are only applicable when the enabling conditions can be replicated in REDD+ contexts. There is a need for more empirical evidence to support PMRV claims on achieving REDD+ social outcomes, which may be addressed with more opportunities and rigorous methods for assessing REDD+ social outcomes. Integrating future PMRV studies into local REDD+ implementations may help create those opportunities, while increasing the participation of local communities as local REDD+ stakeholders. Further development and testing of participatory reporting framework are required to integrate PMRV data with the national database. Publication of empirical PMRV studies is encouraged to guide when, where and how PMRV should be implemented.

SUBMITTER: Hawthorne S

PROVIDER: S-EPMC5094709 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Assessing the Claims of Participatory Measurement, Reporting and Verification (PMRV) in Achieving REDD+ Outcomes: A Systematic Review.

Hawthorne Sandra S Boissière Manuel M Felker Mary Elizabeth ME Atmadja Stibniati S

PloS one 20161103 11

Participation of local communities in the Measurement, Reporting and Verification (MRV) of forest changes has been promoted as a strategy that lowers the cost of MRV and increases their engagement with REDD+. This systematic review of literature assessed the claims of participatory MRV (PMRV) in achieving REDD+ outcomes. We identified 29 PMRV publications that consisted of 20 peer-reviewed and 9 non peer-reviewed publications, with 14 publications being empirically based studies. The evidence su ...[more]

PMID: 27812110

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Project description:OBJECTIVE:To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians. DESIGN:Systematic review. DATA SOURCES:Medline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019. ELIGIBILITY CRITERIA FOR SELECTING STUDIES:Randomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning research. The main distinguishing feature of convolutional neural networks (CNNs) in deep learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition. The algorithm learns for itself the features of an image that are important for classification rather than being told by humans which features to use. The selected studies aimed to use medical imaging for predicting absolute risk of existing disease or classification into diagnostic groups (eg, disease or non-disease). For example, raw chest radiographs tagged with a label such as pneumothorax or no pneumothorax and the CNN learning which pixel patterns suggest pneumothorax. REVIEW METHODS:Adherence to reporting standards was assessed by using CONSORT (consolidated standards of reporting trials) for randomised studies and TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) for non-randomised studies. Risk of bias was assessed by using the Cochrane risk of bias tool for randomised studies and PROBAST (prediction model risk of bias assessment tool) for non-randomised studies. RESULTS:Only 10 records were found for deep learning randomised clinical trials, two of which have been published (with low risk of bias, except for lack of blinding, and high adherence to reporting standards) and eight are ongoing. Of 81 non-randomised clinical trials identified, only nine were prospective and just six were tested in a real world clinical setting. The median number of experts in the comparator group was only four (interquartile range 2-9). Full access to all datasets and code was severely limited (unavailable in 95% and 93% of studies, respectively). The overall risk of bias was high in 58 of 81 studies and adherence to reporting standards was suboptimal (<50% adherence for 12 of 29 TRIPOD items). 61 of 81 studies stated in their abstract that performance of artificial intelligence was at least comparable to (or better than) that of clinicians. Only 31 of 81 studies (38%) stated that further prospective studies or trials were required. CONCLUSIONS:Few prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions. STUDY REGISTRATION:PROSPERO CRD42019123605.

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Dataset Information

Assessing the Claims of Participatory Measurement, Reporting and Verification (PMRV) in Achieving REDD+ Outcomes: A Systematic Review.

Publications

Assessing the Claims of Participatory Measurement, Reporting and Verification (PMRV) in Achieving REDD+ Outcomes: A Systematic Review.

Similar Datasets

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