The Perinatal Mental Health Project: A Critical Appraisal of Program Viability, Accessibility and Sustainability
Bibliographic record
Abstract
Background. Perinatal depression is one of the leading causes of disability in perinatal women and is highly prevalent in disadvantaged communities in LMICs. However, care capacity remains low in most LMICs. As such, we decided to find and assess a screening program that addresses perinatal mental health problems in a resource-efficient manner. This leads us to a critically appraisal of the Perinatal Mental Health Project (PMHP), a screening program based in peri-urban Western Cape Town that stresses task sharing and stepped care intervention. Method. PubMed, Ovid Medline (1946 to 2018), and Google Scholar were searched for publications until March 2018, with data or evaluation of the PMHP. PMHP website publications were used for data and interpretation. The program’s viability was evaluated based on criteria published by UK National Screening Council. The program’s impact was analyzed using published patient outcome data. Access to care was evaluated at three barriers to accessing care proposed by Gjerdingen et al. (2007). The financial model was evaluated using the “four-pillars” of sustainable organization financial management proposed by León (2001). Findings. The PMHP’s screening program viability satisfies most criteria of the UK National Screening Council, and the program’s benefits outweigh its harms. Patient self-reports indicate successful impact with several highlights in accessibility. The program also demonstrates financial sustainability and potential for scaling-up. Interpretations. The operation model of the PMHP shows satisfactory viability and sustainability. With modifications fitting local context and government cooperation, this model offers promising potential in bringing public health and economic benefits.
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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 | 0.381 | 0.627 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.045 | 0.028 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".