Looking for “normal”: Sense making in the context of health disruption
Bibliographic record
Abstract
This investigation examines perceptions of normality emerging from two distinct studies of information behavior associated with life disrupting health symptoms and theorizes the search for normality in the context of sense making theory. Study I explored the experiences of women striving to make sense of symptoms associated with menopause; Study II examined posts from two online discussion groups for people with symptoms of obsessive compulsive disorder. Joint data analysis demonstrates that normality was initially perceived as the absence of illness. A breakdown in perceived normality because of disruptive symptoms created gaps and discontinuities in understanding. As participants interacted with information about the experiences of health‐challenged peers, socially constructed notions of normality emerged. This was internalized as a “new normal.” Findings demonstrate normality as an element of sense making that changes and develops over time, and experiential information and social contexts as central to health‐related sense making. Re‐establishing perceptions of normality, as experienced by health‐challenged peers, was an important element of sense making. This investigation provides nuanced insight into notions of normality, extends understanding of social processes involved in sense making, and represents the first theorizing of and model development for normality within the information science and sense making literature.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".