A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 176

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016

File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

How much is spent on mental health research: developing a system for categorising grant funding in the UK. | LitMetric

Knowing how much money is invested in funding mental health research, and in which areas, is essential to inform strategy and track trends to achieve the best allocation of limited resources. However, no comprehensive categorisation system for mental health research is available and, therefore, national and international data on mental health research funding are minimal and not comparable. In this Health Policy paper, we consider the complexities involved in generating such data and propose an approach to classify mental health research grants. We then describe a method using search terms and algorithms for automatic identification and categorisation of mental health research grants listed in a major international database (Dimensions, Digital Science). The automated approach was validated using manually categorised grants data from funders based in the UK, which showed that the accuracy of this approach is satisfactory and comparable to manual classification. Finally, we consider areas of research that are difficult to classify, and how the automated approach can be refined using machine-learning. We argue that agreed definitions and automated approaches could facilitate collaborative reporting of mental health research funders nationally and internationally and improve the strategic dialogue in this area of research.

Download full-text PDF

Source
http://dx.doi.org/10.1016/S2215-0366(19)30033-1DOI Listing

Publication Analysis

Top Keywords

mental health
28
health
8
health grants
8
automated approach
8
mental
6
spent mental
4
health developing
4
developing system
4
system categorising
4
categorising grant
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!