Blended Learning as a Transformative Educational Approach for Qualitative Health Research
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.140 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".