Constructing “sense” from evolving health information: A qualitative investigation of information seeking and sense making across sources
Bibliographic record
Abstract
Focusing on information behavior in a context where medical evidence is explicitly evolving (management of the menopause transition), this investigation explored how women interact with and make sense of uncertain health information mediated by formal and informal sources. Based on interviews with 28 information seekers and 12 health professionals ( HPs ), findings demonstrate that participants accessed and valued a wide range of information sources, moved fluidly between formal and informal sources, and trust was strengthened through interaction and referral between sources. Participants were motivated to seek information to prepare for formal encounters with HPs , evaluate and/or supplement information already gathered, establish that they were “normal,” understand and address the physical embodiment of their experiences, and prepare for future information needs. Findings revealed four strategies used to construct sense from health information mediated by the many information sources encountered and accessed on an everyday basis: women assumed analytic and experiential “postures”; they valued social contexts for learning and knowledge construction; information consistency was used as a heuristic representing accuracy and credibility; and an important feature of sense making was source complementarity. Implications for health information literacy and patient education are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".