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Enregistrement W4387420615 · doi:10.1111/tct.13664

Commentary: Imagining possibilities for JEDI in research

2023· article· en· W4387420615 sur OpenAlexaff
Laura Yvonne Bulk, Joanne Kerins, Neera R. Jain

Notice bibliographique

RevueThe Clinical Teacher · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueQualitative Research Methods and Ethics
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésSociologyQualitative researchFraming (construction)ConsciousnessEmpirical researchMedia studiesEpistemologySocial science

Résumé

récupéré en direct d'OpenAlex

This is the final article in a three-paper series focused on enacting a justice, equity, diversity and inclusion (JEDI) lens in qualitative research. Article one described how to conduct research with a JEDI lens even when this is not the central research focus.1 The authors provided tools for awakening your critical consciousness and JEDI-advancing strategies for conceptualising the phenomenon under study, orienting the research paradigm and methods and analysing data. Article two reported an empirical study of internal medicine trainees that exemplified some of the strategies offered in article one.2 The authors demonstrated how trainees' social identities (gender and country of origin) are part of complex identity transitions. In this article, the authorship teams from articles one (Laura and Neera) and two (Joanne) unite to discuss how article two took up a JEDI lens and to explore how the research in article two might shift if the authors enacted alternative JEDI strategies. Our goal is to illuminate the possibilities that open through enacting a JEDI lens in research. We conclude by sharing how we have been transformed by this process and inviting readers to join in this transformation. Starting out in qualitative research, and even more so when seeking to research with a JEDI lens, can be challenging when we have been taught to think in positivist ways.14 We hope that readers will not be deterred by fear of not doing JEDI ‘right.’ Employing just one small strategy is a good start. We invite you to consider these concerns in the research you read, review and conduct. Table 1 offers some strategies to work through the challenges that may arise. As discussed in article one, this is a journey—we encourage a mindset of continual progress, not perfection.1 As the authors of this article, we are conscious that flaws remain in our own scholarship. We continually learn how to be, and work at being, better allies and co-conspirators in the JEDI arena. There are challenges structured into academic institutions where priorities may differ, but as a scholarly community, we can move this forward in a good way. Acknowledge complexity in the ways you analyse and write about data. Draw from theory and methods that help you to identify, appreciate and highlight nuance in your findings and interpretation. Disclose research team dissent.5 Admit limitations. Ask colleagues for feedback. Remember that language matters. Choose words carefully in consultation with others, check understandings and acknowledge that language is contested. Include rationale for language choices in your writing. Research unfamiliar terminology. Read widely and deeply, discuss your understandings with others and cite your guiding sources. Demonstrate your work: explain what you have done and why as well as the grappling you have engaged with. Acknowledge your mistakes. Be open about vulnerabilities among the research team with each other and in writing. Laura Y. Bulk: Conceptualization; data curation; investigation; methodology; project administration; resources; writing—original draft; writing—review and editing. Joanne Kerins: Conceptualization; data curation; methodology; project administration; resources; writing—original draft; writing—review and editing. Neera R. Jain: Conceptualization; data curation; methodology; project administration; resources; writing—original draft; writing—review and editing. The authors are grateful to the scholars, activists, learners and other individuals who have helped shape their critical thinking and learning. They thank Prof. Lara Varpio, Dr. Abby Konopasky and Dr. Katherine Schultz for including this topic in the Triptych series, the opportunity to learn together and for their support in editing these papers. With sincere gratitude, Laura and Neera acknowledge that they are settlers and are privileged to learn, play, work and live on Indigenous lands: Laura on the unceded, ancestral and continually occupied territories of the xʷməθkʷəy̓əm (Musqueam), Sḵwx̱wú7mesh Úxwumixw (Squamish), Tsleil-Waututh (Slay-wa-tuth) and W̱SÁNEĆ (Saanich) Peoples; and Neera recognises the tangata whenua of Aotearoa, in particular Ngāti Whātua Ōrākei. As settlers and tangata tiriti, they recognise their responsibility to address colonial injustices in and beyond health professions education. No conflicts to declare. The authors have no ethical statement to declare.

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)0,196
score de la tête « metaresearch » (Gemma)0,042
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Études des sciences et des technologies
Catégories consensuellesMétarecherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,688
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,1960,042
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,004
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,905
Tête enseignante GPT0,773
Écart entre enseignants0,132 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeThéorique ou conceptuel
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

Citations1
Publié2023
Routes d'admission1
Résumé présentoui

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