Scaling up care for perinatal depression for improved maternal and infant health (SPECTRA): protocol of a hybrid implementation study of the impact of a cascade training of primary maternal care providers in Nigeria
Bibliographic record
Abstract
BACKGROUND: The large treatment gap for mental disorders in low- and middle-income countries (LMIC) necessitates task-sharing approaches in scaling up care for mental disorders. Previous work have shown that primary health care workers (PHCW) can be trained to recognize and respond to common mental disorders but there are lingering questions around sustainable implementation and scale-up in real world settings. METHOD: This project is a hybrid implementation-effectiveness study guided by the Replicating Effective Programmes Framework. It will be conducted in four overlapping phases in maternal care clinics (MCC) in 11 local government areas in and around Ibadan metropolis, Nigeria. In Phase I, engagement meetings with relevant stake holders will be held. In phase II, the organizational and clinical profiles of MCC to deliver chronic depression care will be assessed, using interviews and a standardized assessment tool administered to staff and managers of the clinics. To ascertain the current level of care, 167 consecutive women presenting for antenatal care for the first time and who screened positive for depression will be recruited and followed up till 12 months post-partum. In phase III, we will design and implement a cascade training programme for PHCW, to equip them to identify and treat perinatal depression. In phase IV, a second cohort of 334 antenatal women will be recruited and followed up as in Phase I, to ascertain post-training level of care. The primary implementation outcome is change in the identification and treatment of perinatal depression by the PHCW while the primary effectiveness outcome is recovery from depression among the women at 6 months post-partum. A range of mixed-method approaches will be used to explore secondary implementation outcomes, including fidelity and acceptability. Secondary effectiveness outcomes are measures of disability and of infant outcomes. DISCUSSION: This study represents an attempt to systematically assess and document an implementation strategy that could inform the scaling up of evidence based interventions for perinatal depression using the WHO mhGAP-IG in LMIC. Trial registration This study was registered on 03 December, 2019. https://doi.org/10.1186/ISRCTN94230307 .
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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 | 0.028 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".