Community-informed perspectives of implementing interpersonal psychotherapy for couples to reduce situational intimate partner violence and improve common mental disorders in Mozambique
Bibliographic record
Abstract
Background: High rates of intimate partner violence (IPV) and mental disorders are present in Mozambique where there is a significant treatment gap. We aimed to report Mozambican community stakeholder perspectives of implementing couple-based interpersonal psychotherapy (IPT-C) in preparation for a pilot trial in Nampula City. Methods: = 85) using purposive sampling. We used grounded theory methods to conduct an inductive coding and then deductively applied the consolidated framework for implementation research (CFIR). Results: For the outer setting, local attitudes that stigmatize mental health conditions and norm IPV as well as an inefficient legal system were barriers. Stakeholders expressed high acceptability of IPT-C, although a lack of resources was a structural challenge for the inner setting. Adaptation of the approach to screen for and address potential mediators of IPV was important for adopting a multisectoral response to implementation and planning. Delivering IPT-C in the community and in collaboration with community stakeholders was preferable. Conclusion: Stakeholders recommended multilevel involvement and inclusion of community-based programming. Task shifting and use of technology can help address these resource demands.
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 | 0.007 | 0.008 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".