Cognitive therapy for depression in tuberculosis treatment: protocol for process evaluation of a multicenter hybrid type 1 effectiveness implementation trial in Pakistan
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) remains a significant public health challenge, particularly in low- and middle-income countries (LMICs) such as Pakistan, where TB and depression frequently co-occur, negatively impacting treatment adherence and outcomes. The cognitive therapy for depression in tuberculosis (CONTROL) trial evaluates the effectiveness of a cognitive behavioral therapy (CBT)-based intervention integrated into TB care. Following the Proctor's framework, this process evaluation aims to assess key implementation outcomes of the trial, including acceptability, adoption, feasibility, appropriateness, fidelity, penetration, and sustainability, to inform the potential scale-up of the intervention within Pakistan's routine TB care. METHODS: This mixed-methods process evaluation is embedded within the CONTROL randomized controlled trial conducted in Khyber Pakhtunkhwa, Pakistan. Quantitative data collection will include structured implementation outcome measures such as Intervention Appropriateness Measure (IAM), Acceptability of Intervention Measure (AIM), Feasibility of Intervention Measure (FIM), Applied Mental Health Research (AMHR) group tool, Revised Cognitive Therapy Scale (CTS-R), CBT-content delivery assessment checklist, Client Service Receipt Inventory (CSRI), and trial administrative data logs. Qualitative data will comprise semi-structured interviews and focus group discussions. Data collection will be at three points: 8-, 24-, and 32-week post-randomization across 12 TB care facilities. Quantitative data will be analyzed descriptively, while qualitative data will be analyzed thematically, followed by triangulation of findings. DISCUSSION: The process evaluation will inform the implementation of CBT-based intervention within TB care. It will also identify barriers and facilitators for integrating mental health care in the TB control program for future scale-up. Findings will inform policymakers on the feasibility of incorporating mental health interventions into routine TB care, support improved patient outcomes, and contribute to global implementation science on integrated mental health care for chronic diseases. TRIAL REGISTRATION: ISRCTN10761003. Registered on November 2023.
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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 | 0.062 | 0.051 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.008 |
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".