Using the WHO-AIMS to inform development of mental health systems: the case study of Makueni County, Kenya
Bibliographic record
Abstract
Abstract Background In order to develop a context appropriate in mental health system, there is a need to document relevant existing resources and practices with a view of identifying existing gaps, challenges and opportunities at baseline for purposes of future monitoring and evaluation of emerging systems. The World Health Organization Assessments Instrument for Mental Health Systems (WHO-AIMS) was developed as a suitable tool for this purpose. Our overall objective of this study, around which research questions and specific aims were formulated, was to establish a baseline on mental health system as at the time of the study, at Makueni County in Kenya, using the WHO-AIMS. Methods To achieve our overall objective, answer our research questions and achieve specific aims, we conducted a mixed methods approach in which we did an audit of DHIS records and county official records, and conducted qualitative interviews with the various officers to establish the fidelity of the data according to their views. The records data was processed via the prescribed WHO-Aims 2.2 excel spreadsheet while the qualitative data was analyzed thematically. This was guided by the six domains stipulated in the WHO AIMS. Results We found that at the time point of the study, there were no operational governance, policy or administrative structures specific to mental health, despite recognition by the County Government of the importance of mental health. The identified interviewees and policy makers were cooperative and participatory in identifying the gaps, barriers and potential solutions to those barriers. The main barriers and gaps were human and financial resources and low prioritization of mental health in comparison to physical conditions. The solutions lay in bridging of the gaps and addressing the barriers. Conclusion There is a need to address the identified gaps and barriers and follow up on solutions suggested at the time of the study, if a functional mental health system is to be achieved at Makueni County.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".