Medical, behavioural and social preconception and interconception risk factors among pregnancy planning and recently pregnant Canadian women
Notice bibliographique
Résumé
OBJECTIVES: The objective of this study is to describe the clustering of medical, behavioural and social preconception and interconception health risk factors and determine demographic factors associated with these risk clusters among Canadian women. DESIGN: Cross-sectional data were collected via an online questionnaire assessing a range of preconception risk factors. Prevalence of each risk factor and the total number of risk factors present was calculated. Multivariable logistic regression models determined which demographic factors were associated with having greater than the mean number of risk factors. Exploratory factor analysis determined how risk factors clustered, and Spearman's r determined how demographic characteristics related to risk factors within each cluster. SETTING: Canada. PARTICIPANTS: Participants were recruited via advertisements on public health websites, social media, parenting webpages and referrals from ongoing studies or existing research datasets. Women were eligible to participate if they could read and understand English, were able to access a telephone or the internet, and were either planning a first pregnancy (preconception) or had ≥1 child in the past 5 years and were thus in the interconception period. RESULTS: Most women (n=1080) were 34 or older, and were in the interconception period (98%). Most reported risks in only one of the 12 possible risk factor categories (55%), but women reported on average 4 risks each. Common risks were a history of caesarean section (33.1%), miscarriage (27.2%) and high birth weight (13.5%). Just over 40% had fair or poor eating habits, and nearly half were not getting enough physical activity. Three-quarters had a body mass index indicating overweight or obesity. Those without a postsecondary degree (OR 2.35; 95% CI 1.74 to 3.17) and single women (OR 2.22, 95% CI 1.25 to 3.96) had over twice the odds of having more risk factors. Those with two children or more had 60% lower odds of having more risk factors (OR 0.68, 95% CI 0.52 to 0.86). Low education and being born outside Canada were correlated with the greatest number of risk clusters. CONCLUSIONS: Many of the common risk factors were behavioural and thus preventable. Understanding which groups of women are prone to certain risk behaviours provides opportunities for researchers and policy-makers to target interventions more efficiently and effectively.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».