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Record W1585544831

How can mental health be integrated into health system strengthening?

2011· article· en· W1585544831 on OpenAlexaboutno aff
Rachel Jenkins, Florence Baingana, Raheelah Ahmad, David McDaid, Rifat Atun

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthData scienceMedicineWorld Wide WebComputer sciencePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

This editorial accompanies the last in our series of four articles on mental health and the global health agenda.1 It addresses health system challenges, which impede the improvement of mental health outcomes. The article briefly analyses financial resource flows for mental health, explores health system factors that influence effective delivery of mental health care and provides a set of actions to address them. The four papers published in this series, and their accompanying editorials, have identified a number of health system bottlenecks which impede delivery of effective mental health care in developing countries, including limited investments in mental health, poor health sector infrastructure, limited access to medicines, a shortage of human resources, inadequate support and supervision of health workers and weak capacity at local and central levels in low-income countries to incorporate mental health broadly into health sector plans.1–4 These articles also describe huge gaps in care and highlight in particular the weak human resource planning for primary and secondary care, ineffective human resource retention policies, and a lack of continuing professional development. A number of actions are necessary to effectively address these health system bottlenecks to delivery of mental health care in order to counter the high prevalence of mental disorders, to build mental capital at individual and population level in order to avoid the burden of mental disorder as much as possible and to prepare health systems for projected increases in mental disorders resulting from demographic change. These actions include: more effective integration of mental health into general health policy and essential healthcare packages; health systems research to demonstrate costs and effectiveness of basic mental healthcare packages in different countries and the effectiveness of interventions to address mental disorders; better identification and use of levers and entry points, to enable increased participation of mental health professionals and stakeholders in health sector reforms; policy actions to strengthen the linkage between mental health, public health and intersectoral policies; enhanced involvement of civil society and the community in needs assessment, planning and delivery of mental health and more effective resource mobilisation strategies to address gaps in financing. In addition to country level initiatives, a number of internationally driven actions will help increase awareness for mental health and help foster greater inclusion of mental health in international targets. For international technical agencies and bilateral and multilateral donors the opportunities for engagement with mental health include actions relating to achievement of the Millennium Development Goals (MDGs), development of poverty reduction strategies in low-income countries, development of country assistance strategies and initiatives aimed at strengthening health systems, such as those through the Health Systems Funding Platform5 and the International Health Partnership,6 and improved synergies with financing and delivery of programmes for communicable diseases such as AIDS, tuberculosis and malaria that substantially affect mental health affected individuals. Mental disorders create a substantial burden of disease in low-and middle-income countries, accounting for around 8% of the total disease burden.7,8 In addition, mental disorders have severe socio-economic consequences for the affected individuals, often pushing them into ‘skid row’ and impoverishment. There is also a growing body of evidence which demonstrates cost-effectiveness of interventions addressing mental disorders9–12 and the affordability of these in low-and middle-income country settings.13 Yet in spite of the substantial burden of disease and its severe socio-economic consequences for affected individuals and their families, and inspite of the evidence base for highly cost-effective interventions to address them, mental health has received very little attention in the international health agenda. Mental health is not included in the MDGs, in spite of its well-demonstrated links to poverty, education, gender, infectious disorders, child health and maternal health. Hence, much needs to be done to address this unawareness and evidence of a policy gap. In addition to generating evidence on what works to address the growing burden of mental disorders in low- and middle-income countries, concerted efforts are needed to ensure that available evidence is translated to policy and practice. Efforts are also needed to ensure that mental health is recognised as an addressable burden by key international agencies and incorporated into the global agendas of the United Nations system and the funding agencies investing in addressing poverty, education, gender, child health and maternal health, as well as communicable diseases and non-communicable disorders, through a number of approaches that include access to effective medicines and preventive interventions, health system strengthening and expansion of human resources for health. In addition to international efforts, country level investment is needed to ensure that mental health is on the agenda of senior policy makers and is included in key national policy and planning meetings on resource allocation, communicable and non-communicable disease and health systems strengthening. In particular, mental health needs to be included in medium-term expenditure frameworks, national health sector strategies and essential packages of health, as well as district health budgets. However, to effectively address mental illness, multisectoral and multi-axial approaches are needed, requiring integration of mental health into social, educational and criminal justice sector dialogue, policies and reforms. International funding could be used to catalyse development of integrated service delivery models that can simultaneously address communicable and non-communicable disease, including mental illness. However, structural changes alone will not solve the burden of communicable and non-communicable diseases, including mental health disorders. Human resources and medicines are needed to expand access to care. Recruitment and retention of human resources for health has been particularly challenging. To address health workforce gaps there is a need for sustained funding to systematically strengthen health systems to expand service coverage through investments in staff, training and equipment. Bilateral organisations with significant budgets such as the Department for International Development (DFID), the Danish International Development Agency (DANIDA), the United States Agency for International Development (USAID), the Japan International Cooperation Agency (JICA), the Swedish International Development Cooperation Agency (SIDA), the Canadian International Development Agency (CIDA), Noraid, Finnaid and organisations such as the Bill and Melinda Gates Foundation are well positioned to take a significant leadership role on mental health (given its significant impacts on general health, education and the economy), in both international fora and in policy dialogue within countries in order to encourage multilateral organisations such as WHO and the World Bank which are still not routinely mainstreaming mental health within their general health sector activities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.034
metaresearch head score (Gemma)0.096
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.034
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.096
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.002
Science and technology studies0.0040.012
Scholarly communication0.0140.029
Open science0.0050.012
Research integrity0.0200.026
Insufficient payload (model declined to judge)0.0180.003

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.

Opus teacher head0.071
GPT teacher head0.319
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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".

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Citations2
Published2011
Admission routes1
Has abstractyes

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