Screening and detection of perinatal depression by non-physician primary healthcare workers in Nigeria
Bibliographic record
Abstract
BACKGROUND: Detection of perinatal depression by healthcare providers remain an important barrier to receiving treatment. This study reports on the detection of perinatal depression by frontline non-physician primary healthcare workers (PHCWs) as well as the feasibility, effectiveness and acceptability of routine screening using the 2-item patient health questionnaire (PHQ-2) during antenatal care. METHOD: Twenty-seven primary healthcare facilities were assigned to screening (n = 11) and non-screening (n = 16) arms. All PHCWs in both arms were trained to diagnose and treat perinatal depression using the WHO mental health gap action intervention guide (mhGAP-IG) while those in the screening arm were trained to routinely screen with PHQ-2 first to determine need for further mhGAP-IG assessment. Perceived usefulness, feasibility and acceptability of routine screening for perinatal depression was explored in key informant interviews on a purposive sample of PHCWs (n = 20) and study participants (n = 22). RESULTS: In the first 6-months following training, the detection rate of perinatal depression was 4.6% at the clinics where PHCW were not routinely screening with the PHQ-2 compared to 11% at the screening clinics. Over the next six months, with refresher training for PHCW in the screening arm and the introduction of monthly supportive supervision for PHCW in both arms, detection rates increased from 4.6 to 7.6% at non-screening clinics and from 11 to 40% at the screening clinics. Over the entire study period only 81 (15.7%) out of the 517 cases of perinatal depression were detected by the PHCWs. Detection of depression by PHCWs was associated with the severity of depression symptoms and routine screening with PHQ-2. The introduction of routine screening was acceptable to both PHCWs and perinatal women. PHCWs reported that the PHQ-2 was useful, easy to administer and feasible for routine use. CONCLUSIONS: Improving detection and subsequently the treatment gap for perinatal depression require not just training of frontline healthcare workers but the introduction of additional measures such as universal screening along with supportive supervision. TRIAL REGISTRATION NUMBER: The main study from which the data for this report was extracted was retrospectively registered 03 December 2019. REGISTRATION NUMBER: ISRCTN 94,230,307.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".