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Enregistrement W4403729200 · doi:10.1093/isr/viae041.7

Implications of COVID-19 to Young Women’s Online Education in Canada

2024· article· en· W4403729200 sur OpenAlexaffabout

Notice bibliographique

RevueInternational Studies Review · 2024
Typearticle
Langueen
DomainePsychology
ThématiqueCOVID-19 and Mental Health
Établissements canadiensMcGill University
Organismes subventionnairesnon disponible
Mots-clésCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Political scienceVirologyMedicineInternal medicine

Résumé

récupéré en direct d'OpenAlex

This paper will explore the gendered implications of online education during the pandemic in Canada. The COVID-19 pandemic shifted the educational environment by transporting in-person social developments within conventional school structures into an online and unfamiliar environment (Bilodeau, Kehler, and Minnema 2021). In this new setting, young women face a range of barriers to education and exacerbated forms of cyberbullying (Kusumawaty et al. 2021; Mkhize and Goapl 2021). Moving forward with the Women, Peace, and Security (WPS) agenda and efforts for recovery (as mentioned by K.C. and Mackenzie in the forum introduction), Canada’s COVID-19 educational recovery plan requires a comprehensive gendered perspective, with attention to the impacts of COVID-19 on young women’s education. This paper explores the intersectional gendered impacts of educational shifts associated with COVID-19, with attention to unpaid labor, unequal access to technology, unreliable access to health resources, and cyberbullying. Although these were preexisting issues for young women, this paper highlights how the pandemic exacerbated them in ways that could have long-term implications for education quality and equity. Conducting schooling online and within the home has contributed to the re-traditionalization of gender roles. Young women students are more likely to be relegated to unpaid and additional home-making labor during periods of school closures (Reliefweb 2021; Canadian Partnership for Women and Children’s Health 2022). This impedes their ability to balance educational responsibilities with home-making duties. Elevated rates of stress and decreasing resilience to educational changes can lead to education disengagement (Whitley, Beauchamp, and Brown 2021), resulting in poorer results in reading, math, and sciences among youth (Frenette, Frank, and Deng 2020). These factors are compounded by attitudinal factors toward the expectation of women to maintain the home while minimizing the need for digital skills (Webb et al. 2021). ​This argument corroborates Azmi’s findings (from this forum) on women migrant returnees who face diverse economic and domestic challenges upon their return home during the pandemic, intensified by existing inequalities. Research indicates that a decrease in access to quality education over time can lead to sustained disadvantages in future vocational opportunities (Maldonado and De Witte 2022; Sabates, Carter, and Stern 2023). While this analysis warns of the impact from gender roles and domestic challenges to education, further studies are required to determine the long-term gender-based educational fallout associated with the pandemic. The challenges in pivoting to online platforms of learning are also tied to systemic unequal access to technologies. In 2020, 11 percent of Canadians did not have sustained access to the internet—predominantly in remote communities (Fowler 2020). A total of 63 percent of Canadians in the lowest income quartile have less than one internet-enabled device per person, posing barriers to routine online education in multichild households (Frenette, Frank, and Deng 2020). Adapting to online learning and new digital literacy skills is an evident barrier for young women in remote communities (Crompton et al. 2021). In particular, Indigenous communities in Canada have experienced greater disadvantages in accessing costly and unstable online connectivity in remote areas (Whitley, Beauchamp, and Brown 2021) and sufficient multimedia curricula. This is compounded by historic discrimination, the failure to support indigenous languages in education, and a lack of internet infrastructure (Human Rights Watch 2021). At large, gender-based discriminatory educational practices prevailed, where gender-aware digital educational curricula were insufficient; young and racialized women were underpresented and misrepresented in multimedia teaching materials (Crompton et al. 2021). Gendered stereotypes were evident within the curriculum and content for digital literacy skills, and boys were favored in developing technological skills (Crompton et al. 2021). Outdated and insufficient digital curriculua can exacerbate inequalities faced by young Indigenous girls and women in their communities. A pivot online has changed the support structure for gendered forms of violence, such as diminished in-person schooling, social support through teachers, classmates, and programming. Institutional gender-positive and sexual education programming in schools decreased during the pandemic (Action Canada 2020). A lack of reliable technology for vulnerable communities can further increase barriers to sexual and reproductive health resources, placing women at higher risk of sexual harm (Canadian Partnership for Women and Children’s Health 2022). While online healthcare resources can improve overall accessibility, vulnerable populations also face challenges to medical privacy (Prokopenko and Kevins 2020). 2SLGBTQIA+ youth face an increased risk of violence if they are restricted to quarantine in homes that are homophobic or unsupportive, which poses barriers to accessing appropriate healthcare (Prokopenko and Kevins 2020). The COVID-19 pandemic increased rates of cyberbullying, where youth represented the largest demographic of online users (Kusumawaty et al. 2021). Cyberbullying can emerge in social networking and chat platforms and gaming websites (Kusumawaty et al. 2021)—forums that were often used to maintain social interaction throughout the pandemic. While online spaces may benefit community-building, they can also facilitate harm. Increasing reports of online harm were reported throughout the pandemic. In particular, Asian communities faced high rates of online hate during the pandemic (Sakamoto et al. 2023); 30 percent of 2SLGBTQIA+ Canadian students have experienced cyberbullying, as compared to 8 percent of heterosexual students (Stonebanks 2021). Cyberbullying and violence against women intersect, where women are at increased risk of forms of online sexual exploitation, coercive sexting, and doxing (Crompton et al. 2021). During the pandemic, women faced misogynistic messaging, gender-based harassment and online stalking, and “zoombombing”: the targeted distribution of unsolicited pornographic content on platforms prevalently used for online education (UN Women 2020). Before the COVID-19 pandemic, young women faced existing gender-based challenges to education. Each of the discussed issues—unpaid labor, unequal access to technology, unreliable access to health resources, and cyberbullying—are preexisting issues for women that interconnectedly reinforce mutual harm and impact education quality. The intersection of technological challenges and access to education is compounded when they are jointly implicated in the pandemic. These challenges are not uniform but experienced differently and exacerbated by 2SLGBTQIA+, racialized, and indigenous women. As students transition back to in-person learning, Canada’s educational recovery plan must consider the varying experiences of young women. A robust yet rigorous approach to education will tackle the varying needs of diverse communities. Policies must prioritize a holistic perspective on education that include all aspects of wellbeing. For example, Indigenous women and girls face systemic and disproportionate challenges in access to clean water and increased rates of food insecurity and sexual violence (Heck, Eaker, and Franco 2021). While these elements are not conventionally educational, these needs must be met to sustain fulsome schooling participation. Curriculum development must consider diverse household structures beyond the nuclear household—lower-income and single-parent households face increased barriers to education and technological resources. Canada must consider the pervasiveness of technology in everyday life and remain prepared for future instances of increased online learning. While Canada has adopted programs to support infrastructure development for stable internet access, the COVID-19 pandemic has demonstrated the grave impact that a lack of internet can pose on education. It is imperative that infrastructure development should adopt a gender-based approach in considering underserved communities that are at increased risk of education drop-out rates. Schools must consider digital education programming and organize accessibility programs to provide technological devices for lower-income households. Attitudinal shifts are required to support young women’s fulsome education and decrease gendered cyberbullying. This includes debunking gendered stereotypes around digital literacy skills and caretaking roles. Schools can promote attitudinal shifts through early digital literacy programs and providing workshops on cyber hygiene and communication etiquette. As Canada’s educational recovery plan for COVID-19 continues to evolve, it must address these outlined challenges to ensure a holistic and inclusive approach to young women’s education.

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)0,003
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,159
Score d'incertitude au seuil0,975

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,003
Études des sciences et des technologies0,0330,007
Communication savante0,0070,002
Science ouverte0,0020,008
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0120,001

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 Opus0,128
Tête enseignante GPT0,540
Écart entre enseignants0,412 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations0
Publié2024
Routes d'admission2
Résumé présentoui

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