Blended Learning as a Transformative Educational Approach for Qualitative Health Research
Notice bibliographique
Résumé
Background: Qualitative health research seeks to elucidate the realities of context, reveal the complexities of behaviour, probe the intersecting and multiple determinants of health at individual, community and institutional levels, and capture the dynamics of health care provision from the perspectives of patients, providers, and systems. Traditionally, in our Family Medicine Department at McGill University, graduate students are trained in qualitative health research in the context of a synchronous in-person classroom. Amidst the pandemic, synchronous learning shifted to online modalities, obliging rapid innovation in pedagogic practice. Careful consideration and creation of new online modalities for engaged student learning took place, and when implemented, instructor and student feedback was solicited on whether or how they were effective. Together, co-instructors and the teaching assistant for the course reflected on the challenges and opportunities of teaching qualitative research in an online environment, and how online modalities might be usefully blended with in-person learning. Reflections: Three arguments supporting a blended approach were identified. Firstly, blending online and in-person approaches enables learners to tailor their educational experience to their needs and objectives, and to some extent, control the content, sequence, pace, and time of their learning. Secondly, blended learning empowers educators by offering tools and systems to monitor learner progress, while encouraging creativity in conveying content that may be complicated and dense (e.g., providing online workshops about managing qualitative data analysis via readily accessible online software). Lastly, blended learning has the potential to transform graduate training for the better by facilitating innovative modes of communication (e.g., use of chat function in videoconferencing software and online discussion boards as modalities for discussion that engage students who may not otherwise speak), enabling students to contextualize their projects (e.g., implementation of an observational data collection assignment, unique to each student based on where they live and their interests), while better balancing their academic, professional, and personal lives. Discussion: To develop a thorough understanding of qualitative health research, key concepts can be taught and practiced through a combination of in-person and online synchronous and asynchronous learning modalities. In doing so, educators can take advantage of innovative learning technologies, while also maintaining the humanistic touch necessary for education to be meaningful and effective. Importantly, from our experiences we note that blended learning approaches are viable and pertinent in the context of qualitative health research, an idea that was previously dismissed due to perceptions that qualitative inquiry and learning requires solely in-person, hands-on, engagement.
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 | 0,140 | 0,153 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,006 | 0,011 |
| Communication savante | 0,011 | 0,006 |
| Science ouverte | 0,008 | 0,024 |
| Intégrité de la recherche | 0,004 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,035 | 0,005 |
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 ».