Qualitative Research Studies Online: Using Prompted Weekly Journal Entries During the COVID-19 Pandemic
Bibliographic record
Abstract
Solicited journal entries are a qualitative research method with a fairly strong tradition in sociological research and particularly in qualitative health research. However, the practices and strengths associated with solicited journal entries have not been explored as frequently or comprehensively as more conventional qualitative research methods, such as interviews. During the COVID-19 pandemic we carried out two online studies employing solicited written journal entries and photos. One study focused on pregnancy and health care experiences during the pandemic and the other on everyday life while working from home due to public health restrictions. Here, we discuss solicited online journal entries as a qualitative method and reflect on the strengths and challenges we encountered, including those related to using the online survey tool LimeSurvey for a qualitative diary-based study. The richness of data and the ability to solicit participants' contemporaneous reflections over the course of a set length of time, the ability to reach people across time zones and in multiple places, and the ability to adapt prompts in a quickly changing research context are major strengths of online journaling. The level of commitment required by participants, the potential for attrition, the need for literacy and technology access, and the large amount of data from each participant are potential limitations for researchers to consider.
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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 | 0.047 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".