Suicide attempt survivors’ experiences with mental health care services: A mixed methods study.

2020 ◽  
Vol 51 (2) ◽  
pp. 172-183
Author(s):  
Melanie A. Hom ◽  
Evan A. Albury ◽  
Marielle M. Gomez ◽  
Kirsten Christensen ◽  
Ian H. Stanley ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mixed Methods ◽  
Suicide Attempt ◽  
Mental Health Care ◽  
Health Care Services ◽  
Mixed Methods Study ◽  
Care Services

scholarly journals Selvmordsforebyggelse: Inddragelse af pårørende

2015 ◽  
Vol 10 (2) ◽  
pp. 132
Author(s):  
Jens Peter Dam Eckardt Jensen
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Early Detection ◽  
Suicide Attempt ◽  
Mental Health Care ◽  
Suicidal Behavior ◽  
Health Care Services ◽  
Care Services ◽  
The Right ◽  
Do So

<p>Preventing suicide: Involvement of relatives</p><p>In addition to the devastating consequences of suicide attempt and suicide contemplation, suicidal behavior also has dramatic consequences for family and loved ones. However, close connections may play an important role in the prevention of suicide if they are capable of aiding the mental health care services with early detection and management. To do so, they should be informed and given the right tools. This article sums up existing knowledge and recommendations on the topic but further studies are needed before definite conclusions can be drawn.</p>

Mental health economics: current methodological issues

2011 ◽  
Vol 20 (3) ◽  
pp. 239-243 ◽  
Author(s):  
P. McCrone
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Health Care Services ◽  
Self Report ◽  
Economic Evaluations ◽  
Methodological Issues ◽  
Care Services ◽  
Life Years ◽  
Mental Health Economics

Background:Investment in innovative mental health care services requires the use of scarce resources that could be used in alternative ways. Economic evaluation is essential to ensure that such an investment is appropriately compared with investment elsewhere.Method:A non-systematic review of mental health evaluations identifies key methodological issues pertaining to economic studies.Results:Economic evaluations require the measurement and combination of costs and outcomes, and clarity about how this measurement is undertaken is required. Regarding costs, important considerations relate to the perspective to be taken (e.g., health service or societal), method of measurement (patient self-report or use of databases) and valuation (actual costs, fees or expenditure). Decision makers frequently need to compare evidence both within and between clinical areas and therefore there is a tension between the use of condition specific and generic outcome measures. Quality-adjusted life years are frequently used in economic evaluations, but their appropriateness in mental health care studies is still debated.Conclusions:Economic evaluations in the area of mental health care are increasing in number and it is essential that researchers continue to develop and improve methods used to conduct such studies.

scholarly journals A Socio-ecological Framing of the Philippine Mental Health Act of 2017

Spectrum ◽  
2018 ◽  
Author(s):  
Josiah Michael Villareal De Los Santos ◽  
Sonya Jakubec
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Rural Areas ◽  
Health Care Services ◽  
Population Level ◽  
Case Review ◽  
Care Services ◽  
Barriers To Implementation ◽  
Sphere Of Influence

Filipinos experience numerous barriers to mental health care in their country, such as stigmatization ofillness and behaviours, lack of mental health care services, and resource deficits. The Philippine MentalHealth Act of 2017 was formed to resolve these issues and is in its early stages of implementation.Legislation and policy interventions of this nature are but one level of many interventions that can addresshealth care at a population level. The influence of this legislation for different levels of society is analyzed inorder to understand the different barriers and alternatives to its implementation. Solutions suggested in thelegislation, such as addressing lack of accessibility in rural areas, creating liaisons between different levelsof mental health care, and educating the population regarding mental health, are explored for their effects ondifferent spheres, or levels, of influence. The comprehensiveness of the legislation to address the needs ofmental health service users are highlighted, as are barriers to implementation that inhibit the realization ofpractical strategies. This policy case review and analysis informs program development by highlighting thestrengths and weaknesses aligned to the legislative articles’ target sphere of influence and the population.

scholarly journals Identifying Military Veterans in a Clinical Research Database using Natural Language Processing

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Daniel Leightley ◽  
Katharine M Mark ◽  
David Pernet ◽  
Dominic Murphy ◽  
Nicola T Fear ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Care ◽  
National Health Service ◽  
Mental Health Care ◽  
Health Care Services ◽  
Automatic Machine ◽  
Machine Learning Method ◽  
Learning Method ◽  
Care Services

BackgroundThere is a lack of quantitative evidence concerning United Kingdom veterans who access secondary mental health care. This is mainly due to a person&#8217;s veteran status not being routinely collected when they enter the health care system. Main AimThe study aimed to develop an NLP approach for identifying veterans accessing secondary mental health care services using National Health Service electronic health records. MethodsVeterans were identified using the South London and Maudsley Biomedical Research Centre (SLaM) case register – a database holding secondary mental health care electronic records for the South London and Maudsley National Health Service Trust of 300,000 patients. We developed two methods. An NLP and machine learning tool were developed to automatically evaluate personal history statements written by clinicians. ResultsThis study showed that it was possible to identify veterans using the NLP and machine learning approach on a sub-set of 4,200 patients. The automatic machine learning method was able to identify 270 veterans representing an accuracy of 97.2%. It is estimated to take between 6 to 16 minutes to manually search patient history statements whereas the automatic machine learning method took only one minute to run. ConclusionWe have shown that it is possible to identify veterans using NLP combined with machine learning. This work contributes towards the development of a more comprehensive picture of veterans who are accessing secondary mental health care services in the UK. It represents a first step in identifying veterans from one dataset and we hope that future research can inform the possibility of deploying the methods nationally. Despite our success in the current work, the tools are tailored to the SLaM dataset and future work is needed to develop a more agnostic framework. FundingForces in Mind Trust

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