Covid-19 pandemic and postnatal depression, risk factors for postnatal depression
Bibliographic record
Abstract
Objective: To evaluate the effects of Covid-19 pandemic on mental health of women in the perinatal period, with an accent on the intensity of depressive symptomatology, and explore the relationship between the specific aspects of Covid-19 pandemic and the registered risk factors for perinatal depression in women. Methods: The study sample consisted of 54 patients, with heterogeneous demographic characteristics (age, marital status, educational background, socio[1]economic status and religious affiliation) selected from the Cabinet for women with perinatal mental health issues at the University Clinic for Psychiatry in Skopje, where they were treated in the period from January 2020 until December 2021 The included patients met the criteria for the diagnosis F.32 during pregnancy, or F32.01/F32.02 postpartum depression in compliance to the ICD 10, and were treated accordingly with a combined approach using psychological interventions and psychopharmaceutic treatment encompassing antidepressant, anxiolytic, antipsychotic, mood stabilizing drugs or a combination of the above mentioned. The inclusion criteria mandated that they were pregnant or had a child in the past two years. The participants could have been subjected to inpatient, or outpatient treatment, or a combination of both modalities during this time. In order to minimize the risk of infection during pandemic times, and protect the health of participants and researchers, the interviews were conducted via telephone beginning with obtaining an informed consent for participation in the study from the patients who were primarily informed in detail about the aims of the study, their rights and protection of their data and anonymity. We used the following methodology: - A structured, non-standardized socio-demographic questionnaire including 11 items regarding age, parity, realized pregnancies, course of pregnancy and delivery, type of treatment, pharmacological therapy, marital status, socio-economic status, educational background, religious affiliation and presence of previously established risk factors in the world literature; - A structured, non-standardized questionnaire regarding the effects of Covid-19 pandemic consisting of 11 questions; - Edinburgh Depression Perinatal Scale (EDPS) This is a 10-questions self-reported scale, which proved to be a valuable and efficient tool for screening perinatal depression. Namely, a presence of clinical depression was registered in 17.2% of the included participants using the EDPS, the percentage of perinatal depression before the COVID-19 pandemic ranged between 10-14% and has been presented in the literature. Of these, 37.8% were registered in Spain, and 40.7% in Canada [3]. Results: The statistical analysis of data from the structured non-standardized questionnaire for the effects of Covid-19 pandemic showed no statistical significance in the subjectively reported effects from the pandemic on participants’ mental health based on the question from the structured non-standardized Covid-19 questionnaire “Do you feel that the Covid-19 pandemic had an effect on your mental health prior or after the childbirth?” where 55.6% answered with “Yes” and 44.4% with “No”. In spite of this, we gained a perspective of the prevailing risk factors in this population and the correlational analysis of data from the questionnaires granted us insights into further investigation of the relationship between the co-variables.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".