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Enregistrement W4364367860 · doi:10.3389/fpsyt.2023.1188080

Editorial: Neurological and clinical aspects of perinatal mental health

2023· editorial· en· W4364367860 sur OpenAlexaboutno aff
Tom Kingstone, Karen M. Tabb, Yuan‐Pang Wang

Notice bibliographique

RevueFrontiers in Psychiatry · 2023
Typeeditorial
Langueen
DomaineMedicine
ThématiqueMaternal Mental Health During Pregnancy and Postpartum
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute on Minority Health and Health DisparitiesOffice of Research on Women's HealthNational Institute of Mental Health
Mots-clésMental healthPsychiatryPsychologyMedicine

Résumé

récupéré en direct d'OpenAlex

Perinatal mental health refers to a variety of experiences, disorders and diagnoses experienced by women during pregnancy and up to 12 months after birth. During the perinatal period, around 1-in-5 women experience a mental health problem, such as an anxiety disorder (e.g. Generalised Anxiety Disorder, Obsessive-Compulsive Disorder), depression, or an episode of psychosis -estimates vary depending on the type of mental health problem and biological, psychological and social factors (Howard & Khalifeh, 2020). It is clear from existing research that early identification of these problems is important to avoid the exacerbation of symptoms and improve long term outcomes for both mother and baby (Di Venanzio et al. 2017).In the context of COVID-19, emerging evidence indicates an increase in the incidence rates for perinatal mental health problems during the pandemic (Hessami et al. 2020). Such reported increases during COVID-19 exert further pressure on families and health and social care services. These increased burdens, coupled with gaps in our knowledge, highlight the essential need for up-to-date research to inform clinical practice and treatment.The Research Topic 'Neurological and clinical aspects of perinatal mental health' includes eight articles covering a range of perspectives, study methodologies and contexts. Each article makes a novel contribution to extend the evidence-base for perinatal mental health. Four overarching themes are covered:• Suicide is the leading cause of maternal mortality during the perinatal period (Orsolini et al. 2016). Suicidal ideation, a key precursor to suicidal death, with prevalence rates ranging from 3-30% (Gelaye et al. 2016). Evidence for an association between suicidal ideation and depression seems unclear among women during pregnancy (Faisal-Cury et al.). To address this gap in evidence, Faisal-Cury et al.took applied an epidemiological methodology to examine the role of depression in moderating suicidal ideation between pregnant and non-pregnant women in a Brazilian context. The authors describe important implications for clinical practice through and the identification of risk factors for suicide ideation, including a recent diagnosis of clinical depression.Substantive progress has been made in recent years to understand key aspects of the neurobiology of maternal mental illness, such as, neuroplasticity and neuroendocrine and immune system changes (Maguire et al. 2020). However, further research is needed to understand the neurobiological mechanisms underlying specific perinatal mental health disorders, such as antenatal depression. Cheng et al. conducted a voxel-based whole-brain analysis of 43 singleton parents to elucidate the neurobiological features of antenatal depression during the COVID-19 pandemic. Mao et al. examined potential biomarkers for antenatal depression through tandem mass spectrometry methods. The authors aimed to establish potential for a serum metabonomic method in the early diagnosis of antenatal depression. Both primary research studies add to the evidence base for early detection of antenatal depression.There are few validated measures that have been designed specifically to screen for and assess the severity of perinatal anxiety. Validated measures in maternal mental health tend to focus on depression (e.g. Edinburgh Postnatal Depression Scale). Alternatively, standardised measures for anxiety disorders may be used as a proxy for perinatal anxiety (e.g. Generalised Anxiety Disorder-7 item ). The Perinatal Anxiety Screening Scale (PASS) has been developed specifically to screen for anxiety across the perinatal period and include four dimensions: acute anxiety and adjustment, general worry and specific fears, perfectionism, control and trauma, and social anxiety (Summerville et al. 2014). Koukopolos et al. recognised that PASS had not been validated for use among Italian women; their article aimed to address and demonstrate the reliability and validity of PASS for this population. Hicks et al. offers a Canadian "view from the ground" on current clinical practice through their description of findings from a cross-sectional survey of perinatal mental health providers. The authors collate opinions from multiple stakeholders involved in perinatal mental health and describe important gaps in provision linked to training, screening, and management of perinatal mental health problems. This study highlights the need for researchers in perinatal mental health to plan effective dissemination strategies to support translation of research findings and engagement among practitioners.Articles in this Research Topic help to raise awareness about perinatal mental health and key issues related to the early identification -either through risk factors, biomarkers or self-reported symptoms -treatment interventions and service provision. All help to add to the evidence base for neurological and clinical aspects of perinatal mental health. Many of these studies provide evidence for scalable real-world solutions and all identify future areas of research.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,028
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,024
Score d'incertitude au seuil0,081

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,028
Méta-épidémiologie (sens strict)0,0040,001
Méta-épidémiologie (sens large)0,0040,004
Bibliométrie0,0060,002
Études des sciences et des technologies0,0030,003
Communication savante0,0070,006
Science ouverte0,0050,002
Intégrité de la recherche0,0150,014
Charge utile insuffisante (le modèle a refusé de juger)0,0240,013

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,012
Tête enseignante GPT0,341
Écart entre enseignants0,329 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission1
Résumé présentoui

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