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Enregistrement W4200430778 · doi:10.1002/aur.2662

Experiences of student and trainee autism researchers during the <scp>COVID</scp>‐19 pandemic

2021· article· en· W4200430778 sur OpenAlexaff
Sowmyashree Mayur Kaku, Alana J. McVey, Alan S. Gerber, Charlotte M. Pretzsch, Desiree R. Jones, Fathima Kodakkadan, Jiedi Lei, Lauren Singer, Lucy Chitehwe, Rebecca Poulsen, Marika C. Coffman

Notice bibliographique

RevueAutism Research · 2021
Typearticle
Langueen
DomainePsychology
ThématiqueCOVID-19 and Mental Health
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésAutismCoronavirus disease 2019 (COVID-19)PsychologyPandemicMedical educationCoping (psychology)Set (abstract data type)Mental health2019-20 coronavirus outbreakPedagogyMedicineClinical psychologyPsychiatry

Résumé

récupéré en direct d'OpenAlex

Circumstances surrounding the COVID-19 pandemic have resulted in significant personal and professional adjustments. Students and trainees, including those in autism research, face unique challenges to accomplishing their training and career goals during this unprecedented time. In this commentary, we, as members of the International Society for Autism Research Student and Trainee Committee, describe our personal experiences, which may or may not align with those of other students and trainees. Our experiences have varied both in terms of the ease (or lack thereof) with which we adapted and the degree to which we were supported in the transition to online research and clinical practice. We faced and continue to adjust to uncertainties about future training and academic positions, for which opportunities have been in decline and have subsequently negatively impacted our mental health. Students and trainees' prospects have been particularly impacted compared to more established researchers and faculty. In addition to the challenges we have faced, however, there have also been unexpected benefits in our training during the pandemic, which we describe here. We have learned new coping strategies which, we believe, have served us well. The overarching goal of this commentary is to describe these experiences and strategies in the hope that they will benefit the autism research community moving forward. Here, we provide a set of recommendations for faculty, especially mentors, to support students and trainees as well as strategies for students and trainees to bolster their self-advocacy, both of which we see as crucial for our future careers. LAY SUMMARY: The COVID-19 pandemic has affected students and trainees, including those in autism research, in different ways. Here, we describe our personal experiences. These experiences include challenges. For example, it has been difficult to move from in-person to online work. It has also been difficult to keep up with work and training goals. Moreover, working from home has made it hard to connect with our supervisors and mentors. As a result, many of us have felt unsure about how to make the best career choices. Working in clinical services and getting to know and support our patients online has also been challenging. Overall, the pandemic has made us feel more isolated and some of us have struggled to cope with that. On the other hand, our experiences have also included benefits. For example, by working online, we have been able to join meetings all over the world. Also, the pandemic has pushed us to learn new skills. Those include technical skills but also skills for well-being. Next, we describe our experiences of returning to work. Finally, we give recommendations for trainees and supervisors on how to support each other and to build a strong community.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,475
Score d'incertitude au seuil0,728

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,314
Tête enseignante GPT0,543
Écart entre enseignants0,229 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
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

Citations3
Publié2021
Routes d'admission1
Résumé présentoui

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