Quality of perinatal depression care in primary care setting in Nigeria
Bibliographic record
Abstract
BACKGROUND: Even though integrating mental health into maternal and child health (MCH) is widely accepted as a means of closing the treatment gap for maternal mental health conditions in low- and middle-income countries (LMIC), there are not many studies on the quality of the currently available mental health care for mothers in these countries. This study assessed the existing organization of service for maternal mental health, the actual care delivered for perinatal depression, as well as the quality of the care received by affected women presenting to primary care clinics in Ibadan, Nigeria. METHODS: The Assessment of Chronic Illness Care (ACIC) tool was administered to the staff in 23 primary maternal care clinics and key informant interviews were conducted with 20 facility managers to explore organizational and administrative features relevant to the delivery of maternal mental health care in the facilities. Detection rate of perinatal depression by maternal care providers was assessed by determining the proportion of depressed antenatal women identified by the providers. The women were then followed up from the antenatal period up until 6 months after childbirth to track their experience with care received. RESULTS: All the facilities had ACIC domain scores indicating poor capacity to offer quality chronic care. Emerging themes from the interviews included severe manpower shortage and absence of administrative and clinical support for manpower training and care provision. Only 31 of the 218 depressed women had been identified by the maternal care providers as having a psychological problem throughout the follow-up period. In spite of the objective evidence of inadequate care, most of the perinatal women rated the service provided in the facilities as being of good quality (96%) and reported being satisfied with the care received (98%). CONCLUSION: There are major inadequacies in the organisational and administrative profile of these primary maternal care facilities that militate against the provision of quality chronic care. These inadequacies translate to a large treatment gap for women with perinatal depression. Lack of awareness by service users of what constitutes good quality care, indicative of low service expectation, may hamper user-driven demand for quality improvement.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".