Turning intimate spaces into digital classrooms: Public health students’ experiences of learning and doing critical qualitative research in pandemic times
Bibliographic record
Abstract
Throughout the COVID-19 pandemic, burgeoning health researchers have been tasked with learning how to conduct critical qualitative health research from the intimate spaces of their homes. In this presentation, we, three public health students, highlight our experiences as learners and doers of critical qualitative methods to demonstrate the challenges and triumphs that co-exist with pursuing our academic goals during a global pandemic. We employ a critical, reflexive narrative approach to storying our experiences of learning and practicing critical qualitative health research methods from the (dis)comfort of our homes during the pandemic. Using diverse theoretical lenses, including embodiment and poststructuralism, we story our experiences of navigating the blurry boundaries created by our necessary participation in the digital world. We construct and present these stories using arts-based qualitative research methods that were introduced to us in our courses and readings through the Centre for Critical Qualitative Health Research. Stitching together words to create poetry, fabric to construct a quilt, and pictures and writing to articulate experiences, these stories explore how the transformation of our intimate space into an academic and research space impacts the experience of learning to be a critical qualitative health researcher. David uses creative analytic writing practices through memos and journals, paired with photography, to express his experience as a learner. He draws on embodiment, attuning himself to the body to better understand his experiences. Through the medium of poetry, Madison grapples with the reality of being a digital student - letting her anxieties, curiosities, and questions stumble out to make sense of her virtual self. Finally, Kristie uses quilting to construct a material representation of the Zoom experience, exploring the intimacy and alienation of the digital classroom. Through our stories, we hope to create space to deeply consider what it means to be an online learner and an online being. Through these three interwoven stories, we expose our vulnerabilities to carve out space to reflect on our needs and desires to thrive in digital learning environments.
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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 | 0.038 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.038 | 0.052 |
| Scholarly communication | 0.030 | 0.020 |
| Open science | 0.006 | 0.044 |
| Research integrity | 0.009 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".