Transforming access to care for serious mental disorders in slums (the TRANSFORM Project): rationale, design and protocol
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper introduces the TRANSFORM project, which aims to improve access to mental health services for people with serious and enduring mental disorders (SMDs - psychotic disorders and severe mood disorders, often with co-occurring substance misuse) living in urban slums in Dhaka (Bangladesh) and Ibadan (Nigeria). People living in slum communities have high rates of SMDs, limited access to mental health services and conditions of chronic hardship. Help is commonly sought from faith-based and traditional healers, but people with SMDs require medical treatment, support and follow-up. This multicentre, international mental health mixed-methods research project will (a) conduct community-based ethnographic assessment using participatory methods to explore community understandings of SMDs and help-seeking; (b) explore the role of traditional and faith-based healing for SMDs, from the perspectives of people with SMDs, caregivers, community members, healers, community health workers (CHWs) and health professionals; (c) co-design, with CHWs and healers, training packages for screening, early detection and referral to mental health services; and (d) implement and evaluate the training packages for clinical and cost-effectiveness in improving access to treatment for those with SMDs. TRANSFORM will develop and test a sustainable intervention that can be integrated into existing clinical care and inform priorities for healthcare providers and policy makers.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it