Global mental health commentary: using innovation to create a workforce to deliver and implement culturally adapted CBT in Pakistan
Bibliographic record
Abstract
Most low- and middle-income countries (LMICs) have poor or non-existent mental healthcare. Many of LMIC countries allocate less than 1% of their health budgets to addressing mental illness, making large-scale public health interventions not a practical option, at least for the foreseeable future. Psychiatric services are limited to large urban centres, and mental health literacy is low. There is increasing international recognition of the need to build capacity to strengthen mental health systems in LMICs.The aim of this paper is to offer a reflective commentary on our research undertaken over 15 years in Pakistan psychiatric services to create a workforce and culturally adapted cognitive behaviour therapy (CBT) model for LMICs that works for diverse communities served. The exemplar of our work discussed in this article can be used as lessons for developing mental health therapies for LMICs and other countries with diverse communities globally. Our discussion is based largely, if not, on all aspects describing the key barriers and facilitators to implementation of a workable culturally adapted CBT model for use in Pakistan or any similar LMICs. We report on the implementation of culturally adapted CBT in Pakistan over the past 15 years to improve the identified gaps in evidence. We also highlight the successful dissemination strategies our group employed for successful adaption and implementation.
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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 | 0.020 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.034 | 0.035 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".