Using Solicited Written Qualitative Diaries to Develop Conceptual Understandings of Sleep
Bibliographic record
Abstract
This article presents the use of the solicited written qualitative sleep diary as a method for understanding sleep experience. The article reviews selected quantitative and qualitative research to contextualize contemporary sleep circumstances and concerns. It continues with a summary of previous diary applications in social scientific sleep research. The article then outlines the diary template, data collection procedures, and sampling. Diaries were received from a sample of 48 university students at a Canadian university campus who enrolled in a fourth-year course on sleep and society. The method’s analytical potential is highlighted by student accounts that construct meanings of sleep around academic accomplishments which are conceptualized as “rituals of obligation.” Students interpret sleep as a shutting off from ritual obligation. “Shut off” is observed in four ways: personalized management techniques, calendar rhythmicity, introspective bargaining, and the sleep fritter. The methodological discussion of the sleep diary echoes previously articulated observations about the method while offering additional strengths, precautions, and possibilities relating to the diary’s trustworthiness as a prioritized data collection method. The solicited written qualitative sleep diary benefits from its access and flexibility of participation, the encouragement of creative expression, the adoption of incentives and support mechanisms, and research reflexivity.
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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 | 0.058 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".