Demographic Characteristics of Participants in Studies of Risk Factors, Prevention, and Treatment of Postpartum Depression
Bibliographic record
Abstract
Objectives: Metaanalyses have found that sociodemographic variables are not strong predictors of postpartum depression. However, no studies have systematically examined the extent to which the samples used in published research on postpartum depression have included sufficiently diverse samples of women to merit this conclusion. The objectives of this study were to examine the demographic characteristics of participants in previously published studies and to document existing gaps in the current literature. Method: We extracted age, ethnicity, relationship status, and socioeconomic status of 51 453 participants from 143 studies previously selected for systematic literature reviews. Results: Few studies reported complete demographic data; however, existing data indicate that participants were predominantly aged 25 to 35 years, white, partnered, and of mid- or high-socioeconomic status. Conclusions: To assess the external validity of the findings, improved reporting of demographic characteristics is required in publications related to postpartum depression. Additional research is needed to understand postpartum depression among understudied populations. Objectifs: Les méta-analyses ont révélé que les variables sociodémographiques ne sont pas des prédicteurs fiables de la dépression postpartum. Cependant, aucune étude n'a examiné systématiquement la mesure dans laquelle les échantillons utilisés dans les études publiées de la dépression postpartum incluaient des échantillons suffisamment diversifiés de femmes pour arriver à cette conclusion. Les objectifs de la présente étude étaient d'examiner les caractéristiques démographiques des participantes à des études déjà publiées et à documenter les écarts existant dans la documentation actuelle. Méthode: Nous avons extrait l'âge, l'origine ethnique, l'état relationnel et le statut socio-économique de 51 453 participantes à 143 études préalablement sélectionnées pour des revues systématiques de la documentation. Résultats: Peu d'études ont présenté des données démographiques complètes; cependant, les données existantes indiquent que les participantes étaient majoritairement âgées de 25 à 35 ans, blanches, en couple, et de statut socio-économique moyen à élevé. Conclusions: Une meilleure présentation des caractéristiques démographiques est nécessaire dans les publications liées à la dépression postpartum pour évaluer la validité externe des résultats. Il faut plus de recherche pour comprendre la dépression postpartum chez les populations sous-étudiées.
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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 | 0.030 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.019 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".